Summary Orca Progressive Learning from Complex Explanation Traces arxiv.org
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Orca is a machine learning model developed by Microsoft Research that outperforms other open-source models in instruction following and TruthfulQA tasks, using imitation learning and diverse imitation data with rich signals from GPT-4, while covering 29 distinct skills and respecting user privacy and consent.
Key Points
- Microsoft Research has developed Orca, a 13-billion parameter machine learning model that imitates the reasoning process of large foundation models (LFMs) through imitation learning.
- Orca outperforms conventional instruction-tuned models such as Vicuna-13B and ChatGPT on the BBH benchmark and shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT.
- Orca learns from diverse imitation data with judicious sampling and selection, guided by teacher assistance from rich signals from GPT-4 including explanation traces, step-by-step thought processes, and other complex instructions.
- Orca addresses challenges such as small scale homogeneous training data and a lack of rigorous evaluation by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard (BBH) and AGIEval.
- Orca Progressive Learning is a system that uses complex instructions and explanations for progressive learning in pre-trained language models (LFMs).
- Orca outperforms Vicuna in instruction following and TruthfulQA tasks, but trails behind ChatGPT and GPT-4.
Summaries
289 word summary
The document discusses Orca, a language model trained to mimic GPT-4 that outperforms other open-source models in instruction following and TruthfulQA tasks. Orca generates almost equivalently neutral content as GPT-4 and tends to generate more neutral content than Vicuna and ChatGPT. It performs well on causal judgment, temporal and spatial reasoning, and disambiguation QA tasks using the TrutfulQA-MC 19 dataset. Orca shows a 10-point improvement over Vicuna in overall score assessed by GPT-4 across all datasets and outperforms Vicuna by 42%. Orca is an AI assistant system that can answer questions, choose suitable answers based on common sense, and calculate medians. It uses large-scale training data to generate detailed and easy-to-understand explanations for complex tasks while respecting user privacy and consent. The system is designed to help people find information and answer multiple-choice questions while outputting the correct answer(s) and explaining why other answers are wrong. The research addresses challenges in instruction tuning of small models with LFMs and proposes richer signals to improve distillation performance. Orca's ability to outperform a wide range of foundation models is showcased in zero-shot settings across a broad spectrum of tasks. Orca is a machine learning model developed by Microsoft Research that uses imitation learning to outperform conventional models by imitating large foundation models' reasoning process. It learns from diverse imitation data with rich signals from GPT-4, including explanation traces, step-by-step thought processes, and other complex instructions. Orca addresses challenges by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard and AGIEval. The system covers 29 distinct skills with varying levels of difficulty, including math, academic writing, debugging, code generation, and reasoning abilities. Orca uses teacher assistance and progressive learning from easier to harder examples and is smaller in size than GPT-4.
508 word summary
Orca is a machine learning model created by Microsoft Research that uses imitation learning to imitate large foundation models' reasoning process. It outperforms conventional instruction-tuned models and learns from diverse imitation data with teacher assistance from rich signals from GPT-4, including explanation traces, step-by-step thought processes, and other complex instructions. Orca addresses challenges by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard and AGIEval. The system generates a response based on user instructions and input and uses complex instructions and explanations for progressive learning in pre-trained language models. It covers 29 distinct skills with varying levels of difficulty, including math, academic writing, debugging, code generation, and reasoning abilities. Orca uses teacher assistance and progressive learning from easier to harder examples and is smaller in size than GPT-4.
Orca's generation and reasoning abilities are compared to OpenAI LFMs in case studies, including hate speech detection across different minority groups. The research addresses challenges in instruction tuning of small models with LFMs and proposes richer signals to improve distillation performance. Orca's ability to outperform a wide range of foundation models is showcased in zero-shot settings across a broad spectrum of tasks.
Orca is an AI assistant system that can answer questions, choose suitable answers based on common sense, and calculate medians. It uses large-scale training data to generate detailed and easy-to-understand explanations for complex tasks while respecting user privacy and consent. The system is designed to help people find information and answer multiple-choice questions while outputting the correct answer(s) and explaining why other answers are wrong.
The Flan 2021 and NiV2 collections contain various tasks with millions of queries, and the Zero-Shot Chain-Of-Thought (CoT) collection contains tasks involving math word problem solving, natural language inference, and common-sense reasoning. Orca shows a 10-point improvement over Vicuna in overall score assessed by GPT-4 across all datasets and outperforms Vicuna by 42%. Orca is a language model trained to mimic GPT-4 and performs better than other open-source models. It outperforms Vicuna in instruction following and TruthfulQA tasks, but trails behind ChatGPT and GPT-4. The document discusses biases and weaknesses in off-the-shelf toxicity detectors and emphasizes promoting equality and inclusivity. Orca generates almost equivalently neutral content as GPT-4 and tends to generate more neutral content than Vicuna and ChatGPT. Orca performs well on causal judgment, temporal and spatial reasoning, and disambiguation QA tasks using the TrutfulQA-MC 19 dataset.
There is no mention of the references to papers and reports related to language models, artificial intelligence, and machine learning. The text includes examples of solving compound interest problems, using inequality equations to solve problems, calculating the cost of production, and determining the position of a person in a row of seated individuals. Additionally, there is information about the average human blink rate and estimated number of blinks in a lifetime, as well as a task involving drying 30 clothes. Finally, a group of individuals discusses selling their startup to Microsoft, with one expressing cautious positivity about the benefits of the deal but also expressing concerns about losing control over their business and culture.
740 word summary
The excerpt includes a list of references to papers and reports related to language models, artificial intelligence, and machine learning. A program called Orca is discussed, which can solve compound interest problems and provide formulas for determining doubling time. The text also includes examples of using inequality equations to solve problems, as well as a formula for calculating the cost of production and total income from selling n items. Additionally, information is given about the average human blink rate and estimated number of blinks in a lifetime, as well as a task involving determining the position of a person in a row of seated individuals and the amount of time it would take to dry 30 clothes. Finally, a group of individuals discusses selling their startup to Microsoft, with one expressing cautious positivity about the benefits of the deal but also expressing concerns about losing control over their business and culture. Orca is a language model trained to mimic GPT-4 and outperforms other open-source smaller models. Learning from step-by-step explanations could improve model quality, but refined methods and robust evaluation frameworks are needed. Orca outperforms Vicuna in instruction following and TruthfulQA tasks, but trails behind ChatGPT and GPT-4. The document discusses biases and weaknesses in off-the-shelf toxicity detectors and stresses the importance of promoting equality and inclusivity. Orca generates almost equivalently neutral content as GPT-4 and tends to generate more neutral content than Vicuna and ChatGPT. Orca shows good performance on tasks like causal judgment, temporal and spatial reasoning, and disambiguation QA. The dataset used is TrutfulQA-MC 19, which includes 684 multiple choice questions from 38 categories. Orca is a learning model that uses packing and tokenization techniques to optimize the training process for learning from complex explanation traces. It covers 29 distinct skills with varying levels of difficulty, including math, academic writing, debugging, code generation, and reasoning abilities. Orca uses teacher assistance and progressive learning from easier to harder examples and is smaller in size than GPT-4. The model was evaluated against several baselines and optimized for chat using the Vicuna model. Orca performs similarly to Text-da-Vinci-003 and slightly worse than ChatGPT on analytical and reasoning tasks, but significantly worse than GPT-4. Orca shows a 10-point improvement over Vicuna in overall score assessed by GPT-4 across all datasets and outperforms Vicuna by 42%.
Orca Progressive Learning is an AI assistant system that can answer questions, choose suitable answers based on common sense, and calculate medians. It uses large-scale training data to generate detailed and easy-to-understand explanations for complex tasks while respecting user privacy and consent. The system is designed to help people find information and answer multiple-choice questions while outputting the correct answer(s) and explaining why other answers are wrong. The Flan 2021 and NiV2 collections contain various tasks with millions of queries, and the Zero-Shot Chain-Of-Thought (CoT) collection contains tasks involving math word problem solving, natural language inference, and common-sense reasoning.
Orca Progressive Learning is a system that uses complex instructions and explanations for progressive learning in pre-trained language models (LFMs). The system generates a response based on user instructions and input. Orca's generation and reasoning abilities are compared to OpenAI LFMs in case studies, including hate speech detection across different minority groups. The research addresses challenges in instruction tuning of small models with LFMs and proposes richer signals to improve distillation performance. The key contributions are improving Orca's reasoning skills and demonstrating the limitations of auto-evaluation measures overestimating model abilities. Microsoft Research created Orca, a 13-billion parameter machine learning model that uses imitation learning to imitate the reasoning process of large foundation models (LFMs). Orca outperforms conventional instruction-tuned models and shows competitive performance in professional and academic examinations. It learns from diverse imitation data with teacher assistance from rich signals from GPT-4 including explanation traces, step-by-step thought processes, and other complex instructions. Orca addresses challenges by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard (BBH) and AGIEval. The study compares the performance of various language models on reasoning benchmarks using human labels, finding that Vicuna retains only 64% compared to ChatGPT's 92%. The study highlights the importance of not only generating content but also reasoning and comprehension skills in language models. Orca's ability to outperform a wide range of foundation models is showcased in zero-shot settings across a broad spectrum of tasks. The study concludes with a discussion on the potential for models to supervise their own behavior and advancements in model and dataset sizes.
1413 word summary
Microsoft Research has developed Orca, a 13-billion parameter machine learning model that imitates the reasoning process of large foundation models (LFMs) through imitation learning. Orca outperforms conventional instruction-tuned models such as Vicuna-13B and ChatGPT on the BBH benchmark and shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT. Orca learns from diverse imitation data with judicious sampling and selection, guided by teacher assistance from rich signals from GPT-4 including explanation traces, step-by-step thought processes, and other complex instructions. Orca addresses challenges such as small scale homogeneous training data and a lack of rigorous evaluation by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard (BBH) and AGIEval. The study compares the performance of various language models on reasoning benchmarks using human labels, finding that Vicuna retains only 64% compared to ChatGPT's 92%. The study highlights the importance of not only generating content but also reasoning and comprehension skills in language models like Alpaca, WizardLM, and Vicuna. The text also discusses the use of LFMs like ChatGPT and GPT-4 in zero-shot settings across a broad spectrum of tasks, showcasing Orca's ability to outperform a wide range of foundation models. The study concludes with a discussion on the potential for models to supervise their own behavior and advancements in model and dataset sizes. Orca Progressive Learning is a system that uses complex instructions and explanations for progressive learning in pre-trained language models (LFMs). Existing methods for instruction tuning to mimic the output of LFMs exhibit limitations in task diversity, query complexity, and data scaling. Orca aims to overcome these limitations by leveraging complex instructions and explanations for progressive learning. The system generates a response based on user instructions and input. Orca's generation and reasoning abilities are compared to OpenAI LFMs in case studies, including hate speech detection across different minority groups. The research addresses challenges in instruction tuning of small models with LFMs and proposes richer signals to improve distillation performance. The key contributions are improving Orca's reasoning skills and demonstrating the limitations of auto-evaluation measures overestimating model abilities. Orca Progressive Learning is an AI assistant system that can answer questions, choose suitable answers based on common sense, and calculate medians. It uses large-scale training data to generate detailed and easy-to-understand explanations for complex tasks while respecting user privacy and consent. The system is designed to help people find information and answer multiple-choice questions while outputting the correct answer(s) and explaining why other answers are wrong. The Flan 2021 and NiV2 collections contain various tasks with millions of queries, and the Zero-Shot Chain-Of-Thought (CoT) collection contains tasks involving math word problem solving, natural language inference, and common-sense reasoning. The article also highlights the importance of rains in desert environments for plants and animals and how floods are generally considered destructive and dangerous. Orca is a learning model that utilizes packing and tokenization techniques to optimize the training process for learning from complex explanation traces. It covers 29 distinct skills with varying levels of difficulty sourced from open-source projects and forums, including math, academic writing, debugging, code generation, and reasoning abilities. Orca uses teacher assistance and progressive learning from easier to harder examples and is smaller in size than GPT-4. The model was evaluated against several baselines and optimized for chat using the Vicuna model. Orca performs similarly to Text-da-Vinci-003 and slightly worse than ChatGPT on analytical and reasoning tasks, but significantly worse than GPT-4. Orca shows a 10-point improvement over Vicuna in overall score assessed by GPT-4 across all datasets and outperforms Vicuna by 42%. Orca is a language model that performs well on some tasks but struggles with others, particularly those involving more than five objects or persons. Orca outperforms Vicuna and performs marginally better than ChatGPT on aggregate across all tasks on the Big-Bench Hard benchmark. Orca shows good performance on tasks like causal judgment, temporal and spatial reasoning, and disambiguation QA. Orca's performance improvements over Vicuna are shown in parenthesis in BigBench-Hard without CoT. The performance of Orca is compared to other models, including ChatGPT and GPT-4, in various tasks such as table understanding, geometric reasoning, logical deduction, and choosing a movie from a list of choices. The dataset used is TrutfulQA-MC 19, which includes 684 multiple choice questions from 38 categories. Orca outperforms Vicuna in instruction following and TruthfulQA tasks, but trails behind ChatGPT and GPT-4. Orca's outputs are significantly more truthful than Vicuna's and have less tendency to generate toxic content. The document discusses biases and weaknesses in off-the-shelf toxicity detectors and stresses the importance of promoting equality and inclusivity. Larger models like GPT-4 perform significantly better in generating relevant professional summaries with fewer mistakes, but the style of content generated by different models varies significantly. Orca generates almost equivalently neutral content as GPT-4 and tends to generate more neutral content than Vicuna and ChatGPT. Orca is a language model trained on diverse system instructions and has the potential for efficient and scalable tool-augmented LFM systems. It is important to be aware of the limitations and potential biases of large language models, as well as their lack of contextual understanding and transparency. Orca is a language model trained to mimic GPT-4. Data size and coverage are important in aligning smaller models to larger ones, and there is potential for harm or bias in their application. Orca outperforms other open-source smaller models and can match or surpass ChatGPT quality. Learning from step-by-step explanations could improve model quality, but there is a need for more refined methods and robust evaluation frameworks. The Pythagorean theorem is used to solve a math problem involving finding the lengths of the legs of a right triangle. A logic puzzle involves determining the time slot during which Michael could have gone to a football field. To address a Vitamin D deficiency causing rickets, it is important to first identify the deficient vitamin. Fatty fish, fortified milk products, and whole grains are good sources of Vitamin D, calcium, and phosphorus. Sunlight exposure can also help produce more Vitamin D. Paragraph 1: A formula for calculating the cost of production and total income from selling n items is given. An inequality equation is used to find all possible values of n for which the company will make a profit.
Paragraph 2: Orca is a program that can solve compound interest problems and provide formulas for determining doubling time. Examples of using inequality equations to solve problems are given.
Paragraph 3: The number of blinks per minute for the average human is given, and the estimated number of blinks in a lifetime is calculated using this information. The Rule of 72 is explained, and a formula for calculating doubling time is given.
Paragraph 4: The total number of blinks in a lifetime is estimated based on average lifespan and blink rate. Information about daily blink rate and duration is also given.
Paragraph 5: A task involving determining the position of a person in a row of seated individuals is presented. The answer is given as A.
Paragraph 6: A statement about drying clothes is given, indicating the amount of time it would take to dry 30 items under certain conditions. Prof. Yejin Choi, an expert in machine learning, natural language processing, and artificial intelligence, provides steps to measure 6 liters using a 12-liter jug and a 6-liter jug, and to dry 30 clothes. The process of finding roots of the equation x^2 + 3.0x = 0 involves identifying the equation, factoring it, finding the roots, and presenting the roots as ROOTS = [-3, 0]. A group of individuals discusses selling their startup to Microsoft, with Steven expressing cautious positivity about the benefits of the deal but also expressing concerns about losing control over their business and culture. The excerpt includes lists of references to papers and reports related to language models, artificial intelligence, and machine learning, covering topics such as evaluating large language models trained on code, training language models with human feedback, exploring the capabilities of language models beyond the imitation game, fine-tuning language models, large language models and fairness, self-attention distillation for task-agnostic compression of pre-trained transformers, open-source chatbots with high chat quality, and ALPaCA models for language learning. This excerpt is a list of recent research papers and technical reports related to language models and natural language processing. The papers cover various topics, including hate speech detection, improving performance and efficiency of language models, and visual instruction tuning. Each reference includes the title, author(s), publication venue, and year of publication.
4425 word summary
The excerpt contains a list of references to recent research papers and technical reports related to language models and natural language processing. The papers cover topics such as decoupling reasoning from observations, building language models, hate speech detection, and scaling instruction-finetuned language models. Other topics include improving performance and efficiency of language models, generalization via declarative instructions, and visual instruction tuning. Each reference is accompanied by a title, author(s), publication venue, and year of publication. The excerpt is a list of references to recent papers on various topics related to language models and machine learning. Some of the topics covered include fine-tuning language models, large language models and fairness, and self-attention distillation for task-agnostic compression of pre-trained transformers. There are also references to open-source projects, including a chatbot that impresses GPT-4 with 90% chat quality and an ALPaCA model for language learning. The excerpt is a list of references to various papers and reports related to language models, artificial intelligence, and machine learning. It includes authors such as Percy Liang, Tatsunori B. Hashimoto, OpenAI, and others. The references cover topics such as evaluating large language models trained on code, training language models to follow instructions with human feedback, and exploring the capabilities of language models beyond the imitation game. The references also include a human-centric benchmark for evaluating foundation open-source models on slightly harder tasks. Steven agrees with Lucy about the potential benefits of selling their startup to Microsoft, including financial benefits and support for growth. However, he also expresses concern about potential changes to their company culture and losing control over their business. He shares John's concerns about losing what they've built at their startup. Overall, Steven's feelings about selling can be described as cautiously positive. A group of individuals are discussing an offer to sell their startup to Microsoft. John expresses concerns about losing control over their company and culture if they sell, while Lucy and Steven argue that the financial benefits and access to Microsoft's resources could help them grow their business. They ultimately decide to negotiate the terms of the deal to ensure that they retain some control over the company.
The task is to explain how to solve a quadratic equation in simple steps, including identifying the equation, factoring it, finding the roots, and presenting the roots. The output provides a concise summary of each step in solving the equation. To find the roots of the equation x^2 + 3.0x = 0, we can factor the equation as x(x + 3) = 0 and solve for x, resulting in roots of x = 0 and x = -3. The answer can be presented as ROOTS = [-3, 0]. Prof. Yejin Choi is a faculty member at the Paul G. Allen School of Computer Science & Engineering at the University of Washington with expertise in machine learning, natural language processing, and artificial intelligence. To measure exactly 6 liters using a 12-liter jug and a 6-liter jug, you can follow these steps: fill the 12-liter jug completely with water, pour the contents of the 12-liter jug into the 6-liter jug carefully, fill the 12-liter jug again, pour the water from the 6-liter jug into the 12-liter jug until it is full, fill the 6-liter jug again, pour the water from the 6-liter jug into the 12-liter jug, and finally, fill the 6-liter jug completely. To dry 30 clothes, you would need 30 hours, as it takes one hour to dry one cloth based on the given information that it took five hours to dry five clothes. Excerpt 1: To dry 30 clothes completely, it would take 150 hours assuming the conditions for drying remain the same.
Excerpt 2: Given a row of people sitting, with A and P at the ends, R to the left of A, and S and Z in the center, the task is to determine who is sitting to the right of P. The answer is A.
Excerpt 3: The case study involves evaluating Fermi questions related to lifespan variability. The average human blinks about 15-20 times per minute, which translates to approximately 10,512,000 times per year. Assuming an average lifespan of 80 years, we can estimate the total number of blinks in a lifetime to be approximately 840,960,000. However, the actual number of blinks may vary depending on factors such as age, health, and medication use. The average person spends about 6-8 hours per day sleeping and about 16 hours per day awake and alert, during which they blink about 4.8 million times. The average blink duration is about 0.3 seconds. The average human blinks about 15 to 20 times per minute. To determine the number of blinks in a lifetime, we multiply the number of blinks per minute by the number of minutes in a lifetime (72 years or 4,320 minutes). The estimated number of blinks in a lifetime ranges from 64,800 to 86,400. The Rule of 72 is a formula used to estimate the number of years required for an investment to double in value through a fixed annual rate of return. To use the Rule of 72, simply divide 72 by the annual growth rate. For example, if an item grows at a rate of 20% annually, it will take approximately 3.6 years to double in value. The formula for calculating the number of years for an item to double is 38d = 70 / r, where “d” is the number of years and “r” is the annual growth rate. Orca is a program that can solve compound interest problems, and provides the formula to determine how many years it will take for an item to double. The answer to a question about the number of days in a year if the Earth took 100 days to revolve around the sun is 365. Inequality equations can be solved using steps such as writing down the given cost function and income per item, calculating the total income for selling n items, writing down the condition for making a profit, substituting expressions for total income and total cost, and solving the inequality for n. To make a profit, the total income must be greater than the cost of production. The cost of producing n items is given by the formula C = 7n + 350. The total income from selling n items is the product of the selling price and the number of n items. To find the inequality that gives all possible values of n for which the company will make a profit, we need to compare the total income from selling n items with the cost of producing n items. The inequality that gives all possible values of n for which the company will make a profit is n > 70. To solve for n, we can subtract 7n from both sides: 12n > 7n + 350. To state an inequality that gives all possible values of n for which the manager estimates that the company will make a profit, we need to find the quantity of items that generates a profit. The selling price is $12, so the total income is given by the formula: Total Income = Number of items x Selling Price. The cost of producing a quantity of items is given by the formula: Cost of Production = Number of items x Cost per item. To determine the best food option for addressing a vitamin deficiency causing rickets, it is important to first identify the deficient vitamin. Rickets is primarily caused by a deficiency of Vitamin D. The best food sources of Vitamin D are fatty fish, fortified milk products, and whole grains. Butter is the closest source of Vitamin D among the given food options. Oranges are not a significant source of Vitamin D, calcium, or phosphorus. Tomatoes and whole grains are also not good sources of Vitamin D. It is recommended that the woman consume a diet that is rich in Vitamin D, calcium, and phosphorus, which may include foods such as fatty fish, fortified milk products, and whole grains. The woman should also consider getting more sunlight exposure to help her body produce more Vitamin D. A woman with rickets needs to find the food source rich in the specific vitamin causing the deficiency. The given food options are analyzed. A task is given to determine which food is most likely to help with the condition by following instructions step-by-step. A multiple-choice question-answering case study is provided, with an answer of 10am to 12pm for the available time Michael could have gone to the football field. The explanation for why other answers are wrong is also provided. This text excerpt provides two distinct pieces of information. The first part is a logic puzzle that involves determining the time slot during which Michael could have gone to a football field based on several pieces of information. The correct answer is 10am to 12pm. The second part is a math problem involving trigonometry. The problem involves finding the values of the legs of a right triangle based on the given ratio of one leg to another and the Pythagorean theorem. The solution is a=5 and b=12. The Pythagorean theorem is used to determine the lengths of the legs of a right triangle, given that the hypotenuse is 13 and the ratio of the lengths of the legs is 5:12. Letting the lengths of the legs be 5x and 12x, the equation (5x)^2 + (12x)^2 = 13^2 is used to solve for x, resulting in x = 1. Therefore, the lengths of the two legs are 5 and 12. The equation a^2 + b^2 = c^2 is used to find the actual values of a and b, given that c = 13. The value of a is found using the equation 5a = 12b, and the value of b is found using the fact that a + b = 13. The document discusses the development of Orca, a language model trained to mimic the behavior of GPT-4. The authors emphasize the importance of data size and coverage in aligning smaller models to larger ones, as well as the potential for harm or bias in the application of such models. Orca outperforms other open-source smaller models and can match or even surpass the quality of ChatGPT in some settings. However, there remains a need for more refined methods and robust evaluation frameworks. The study highlights the significance of data and imitation techniques and suggests that learning from step-by-step explanations could significantly improve the quality of models regardless of their size. The authors also note the stochasticity introduced by model size, which may lead to the generation of non-deterministic responses. Orca is a language model trained on diverse system instructions. Its performance is correlated with the distribution of its training data, and its performance in zero-shot settings and other contexts remains untested. Without safeguards, there is potential for Orca and other large language models to be misused for generating harmful content. It is important to be aware of the limitations and potential biases of large language models, as well as their lack of contextual understanding and transparency. Orca retains many of the constraints of the LLaMA model family, but has the potential for efficient and scalable tool-augmented LFM systems. Recent works have demonstrated the significant creative reasoning capabilities of GPT-4 and the power of fresh and comprehensive Bing index. Tool-augmented large flexible models (LFMs) have emerged to address the limitations of smaller models, which lose their ability to serve as an effective knowledge base or memory. Larger models like GPT-4 perform significantly better in generating relevant professional summaries with fewer mistakes. However, the style of content generated by different models varies significantly, and the safety of Orca responses can be attributed to its alignment with GPT-4 as well as moderation. Orca generates almost equivalently neutral content as GPT-4 and tends to generate more neutral content than Vicuna and ChatGPT. When given toxic prompts, Orca has less tendency to generate toxic content than Vicuna. Future work includes a comprehensive experiment using other toxicity detectors and human evaluation. The document discusses biases and weaknesses in off-the-shelf toxicity detectors, and presents an experiment where language models are prompted with toxic and benign examples from a subset of the ToxiGen dataset. The text also includes examples of different models generating responses to prompts related to women's achievements and contributions. GPT-4 and Orca follow the prompt format, while ChatGPT and Vicuna generate responses based on their own unique experiences and perspectives. The authors stress the importance of promoting equality and inclusivity in all aspects of society, and recognizing and celebrating the contributions and achievements of women. Orca outperforms Vicuna in instruction following and TruthfulQA tasks, but there is still a gap between Orca and ChatGPT and GPT-4. Vicuna's output format is noisy and takes more time to manually label compared to Orca, ChatGPT, and GPT-4. Orca's outputs are significantly more truthful than Vicuna's and trailing behind ChatGPT and GPT-4. The prompt format for Vicuna and Orca is provided. The prompt format for ChatGPT and GPT-4 includes a length of 1024 tokens, temperature set to 0.7, and sampling during decoding. Evaluation in TruthfulQA is performed using various similarity metrics, and a generation-based evaluation is preferable and more comprehensive than discriminative evaluation. For questions with more than 4 answer choices, only 4 choices have been randomly sampled in TruthfulQA-MC. Questions with less than 4 answer choices have been removed for all models in TruthfulQA-MC. Orca Progressive Learning is a study focused on Truthful Question Answering. The dataset used is TrutfulQA-MC 19, which includes 684 multiple choice questions from 38 categories. The study evaluates Orca's truthfulness in answering questions that mimic human falsehoods and its safety in terms of being helpful, honest, and harmless. The performance of Orca is compared to other models, including ChatGPT and GPT-4, in various tasks such as table understanding, geometric reasoning, logical deduction, and choosing a movie from a list of choices. ChatGPT outperforms Orca in most tasks, except for the five objects task where Orca performs better. The study suggests that more comprehensive evaluations across different dimensions are needed for future work. Orca, a language model based on Transformer architecture, performs better than ChatGPT in movie recommendation and humor usage, but underperforms in tasks that require world knowledge such as sports, artists, and bands. Orca shows good performance on tasks like causal judgment, temporal and spatial reasoning, and disambiguation QA. Orca achieves parity with GPT-4 in multilingual understanding and outperforms ChatGPT in this area. Further evaluation is needed for better assessment of its multilingual abilities. Orca shows substantially better reasoning capabilities in terms of temporal reasoning and spatial reasoning compared to ChatGPT. Orca outperforms ChatGPT on Disambiguation QA and Snarks. Orca's performance improvements over Vicuna are shown in parenthesis in BigBench-Hard without CoT. Orca is a language model that outperforms Vicuna and performs marginally better than ChatGPT on aggregate across all tasks on the Big-Bench Hard benchmark. Orca achieves 4.5% improvement over ChatGPT in the formal fallacies task and performs better at entailment and semantic understanding. Orca's average performance of 49.7% lags GPT-4 by 26%. Orca has a performance gap in geometric reasoning and long-context modeling compared to ChatGPT. Some examples require LaTeX reasoning, reasoning about colored objects, causal judgment, and tracking shuffled objects. The AGIEval benchmark shows the topical breakdown in performance of GPT-4, ChatGPT, and Orca in various exams such as LSAT-RC, SAT-Math, LSAT-LR, SAT-English, LogiQA, AQuA-RAT, and human average. Orca is a model for answering multiple-choice English questions that requires specialized domain knowledge and can handle complex reasoning. It performs well on some tasks but struggles with others, particularly those involving more than five objects or persons. Some examples require reasoning about Tesla batteries or concepts from Chemistry. Orca beats ChatGPT in 325 examples across different tasks, while ChatGPT dominates Orca in 450 examples. The performance of Orca varies significantly based on the type of system message. Scaling the amount of explanation data by 5x with intermediate ChatGPT assistance significantly improves the model performance by 4.5 points on aggregate. GPT-4's performance surpasses all other models by far, but there is still a lot of headroom for improvement. Compared to Vicuna, Orca shows much stronger performance outperforming it on every category with 42% relative improvement on average. The Orca language model performs similarly to Text-da-Vinci-003 and slightly worse than ChatGPT on analytical and reasoning tasks, but performs significantly worse than GPT-4. Orca retains 88% of ChatGPT quality and outperforms Vicuna by 42%. The performance of Orca is measured using accuracy metric and is compared to baseline models on the AGIEval benchmark on multiple-choice English questions. Orca exhibits strong performance for prompts that span across a wide range of generation roles and shows a 10-point improvement over Vicuna on an aggregate. Orca, a candidate model, shows a 10-point improvement over Vicuna in overall score assessed by GPT-4 across all datasets. Orca retains 95% of ChatGPT quality and 85% of GPT-4 quality. The performance of candidate models (e.g., Vicuna, Orca) against ChatGPT (GPT-3.5-turbo) and GPT-4 as reference models is evaluated using standard zero-shot prompting multiple-choice questions. The benchmark assesses foundation models in tasks related to human cognition and problem-solving, including general college admission tests, law school admission tests, and math competitions. AGIEval is a human-centric benchmark that evaluates the general abilities of foundation models. Orca is a model that can learn from complex explanation traces, including math, academic writing, debugging, code generation, and reasoning abilities. It covers 29 distinct skills with varying levels of difficulty sourced from open-source projects and forums. Orca's capability is evaluated through open-ended generation and its ability to reason and comprehend complex reasoning tasks. The evaluation tasks come from different datasets and are evaluated against reference models such as ChatGPT and GPT-4. Vicuna prompts are a set of original prompts used to evaluate chatbot performance based on GPT-4. The prompts cover various aspects of open-ended answering tasks and are rated on a scale of 0 to 10. Two AI assistants are compared based on their helpfulness, relevance, accuracy, and level of details in their responses. Orca is a learning model designed to improve complex reasoning tasks. It was evaluated against several baselines and optimized for chat using the Vicuna model. The evaluation protocol considered writing, comprehension, analytical, mathematical, and logical reasoning abilities. Orca was trained on 20 NVIDIA A100 GPUs with 80GB memory, and the loss was computed only on the tokens generated per sequence given the length distribution of augmented instructions in the training data. The model learns to generate responses conditioned on the system message and focuses on learning from the most relevant and informative tokens. The Orca model utilizes packing and tokenization techniques to optimize the training process for learning from complex explanation traces. The training process involves utilizing teacher assistance and progressive learning from easier to harder examples. Orca is smaller in size than GPT-4 and leverages ChatGPT as an intermediate teacher assistant. Data collection is constrained by rate limits and cost considerations. The article discusses the Orca Progressive Learning method for complex explanation traces. GPT-4 is observed to elicit longer responses compared to ChatGPT. The sampling algorithm for Flan 2021 and T0 collection is described. People living in a desert environment often look forward to rain, as it provides water for drinking, agriculture, and replenishing natural resources. However, it is not logical for people to look forward to floods as they are generally considered destructive and dangerous. Therefore, the sentence "people in the desert often look forward to flood" is not logical. Summary:
Desert rain can be beneficial for plants and animals. Sentence A suggests that people in the desert look forward to floods, while Sentence B suggests they look forward to rain. The task is to determine which sentence is not logical based on the difference between the two sentences. The Flan 2021 collection contains 142 tasks with a total of 440 thousand queries, while the NiV2 contains 1560 tasks with roughly 5 million queries. The Zero-Shot Chain-Of-Thought (CoT) collection contains a total of 18 tasks involving math word problem solving, natural language inference, and common-sense reasoning. Orca is trained on zero-shot queries from the CoT collection. The Orca Progressive Learning system focuses on zero shot and few-shot queries and uses multiple academic datasets. The FLAN-v2 Collection contains five sub-collections with different tasks, each with its own system messages. System messages are designed to query large foundation models for explanation tuning while preserving the ability to generate short and long answers. The distribution of system messages across different sub-collections is shown in Figure 6, and Table 2 lists all the system instructions used to generate the training data. The system is designed to help people find information and answer multiple choice questions by outputting the correct answer(s) and explaining why other answers are wrong. It also provides guidelines for breaking down definitions into small parts and explaining tasks in simple steps. The document discusses a system called Orca that utilizes large-scale training data to generate detailed and long explanations for complex tasks. The system is designed to be an AI assistant that follows instructions well and provides helpful explanations. The training data consists of a diverse set of tasks augmented with complex instructions and rich signals. System messages are used to provide essential context, guidelines, and other pertinent details to the Orca system. The system instructions are also a way of improving the safety of model responses. The system is trained to generate step-by-step reasoning and provide explanations that are easy to understand, while respecting user privacy and consent. Orca Progressive Learning is a system that uses diverse instruction sets to guide the system to form a well-reasoned and cogent response. The system can be trained to answer questions about specific topics, such as identifying a small lake between Windermere and Grasmere. It can also be trained to choose the most suitable answer out of a list of options based on commonsense knowledge. The system can also calculate the median of a given set of data. The Chat Completion API and GPT-4 models are used for interacting with the system. The Orca Progressive Learning system utilizes complex instructions and explanations for progressive learning in pre-trained language models (LFMs). Instruction tuning is a technique that allows LFMs to learn from input and response pairs, and the system generates a response based on user instructions and input. Existing works, such as Alpaca, Vicuna, and WizardLM, have utilized instruction tuning for tasks such as image captioning, visual question answering, and language-only tasks. However, a significant drawback with these works has been limited task diversity and small-scale training data. Orca aims to overcome these limitations by leveraging complex instructions and explanations for progressive learning. Popular models that have been instruction tuned with OpenAI large foundation models include GPT-4, ChatGPT, Vicuna, and Alpaca. Orca's generation and reasoning abilities are compared to OpenAI LFMs in case studies, including hate speech detection across different minority groups. Orca is evaluated using AutoEvaluation with GPT-4, academic benchmarks, and safety evaluation with ToxiGen. ChatGPT is used as a teacher assistant in progressive learning, and FLAN 2022 Collection is utilized for scaling tasks and instructions. Explanation tuning is implemented by augmenting query-response pairs with detailed responses from GPT-4 that explain the reasoning process of the teacher as it generates the response. The research addresses challenges in instruction tuning of small models with LFMs and proposes richer signals like LFM rationales, logits, intermediate representations, and attention states to improve distillation performance. The key contributions are improving Orca's reasoning skills and demonstrating the limitations of auto-evaluation measures overestimating model abilities. Existing methods for instruction-tuning to mimic the output of language generation models (LFMs) like ChatGPT exhibit notable limitations in task diversity, query complexity, and data scaling. Human-contributed conversations in ShareGPT are a valuable source of data, but they also have some limitations. LFMs trained on such natural conversations may capture the style but not the reasoning skills. The Self-Instruct process involves using simple instructions with limited diversity, which can exhibit limitations in diversity and complexity. Recent works employ a variant of self-instruct, which gradually rewrites the initial set of instructions into more complex versions, attempting to overcome some of the method's inherent shortcomings. However, the resulting queries generated through overly similar responses are then removed, and the remaining instructions are reintegrated into the task pool for further iterations. Nonetheless, any low-quality or overly similar responses are removed. The discrepancy between ChatGPT's quality on professional and academic exams and BigBench-hard underscores the limitations of existing evaluation protocols with smaller query sets. This study discusses some of the reasons behind these gaps and proposes strategies for addressing them. The study compares the performance of various language models on reasoning benchmarks using human labels, finding that Vicuna retains only 64% compared to ChatGPT's 92%. The study highlights the importance of not only generating content but also reasoning and comprehension skills in language models like Alpaca, WizardLM, and Vicuna. The text also discusses the use of LFMs like ChatGPT and GPT-4 in zero-shot settings across a broad spectrum of tasks, showcasing Orca's ability to outperform a wide range of foundation models. The study concludes with a discussion on the potential for models to supervise their own behavior and advancements in model and dataset sizes. The document discusses Orca, a machine learning model that learns from step-by-step explanations. It includes various case studies, such as quadratic equation solving and commonsense question answering, and evaluates the model's reasoning and open-ended generation capabilities. The paper also addresses limitations and includes author contributions and conclusions. The authors suggest that learning from complex explanation traces can improve machine learning models and lead to more advanced AI capabilities. Microsoft Research has developed Orca, a 13-billion parameter model that learns to imitate the reasoning process of large foundation models (LFMs) through imitation learning, drawing on the outputs generated by LFMs. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B and ChatGPT on the BBH benchmark and shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot evaluation resulting in overestimating the small model's capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. Orca learns from diverse imitation data with judicious sampling and selection, guided by teacher assistance from rich signals from GPT-4 including explanation traces, step-by-step thought processes, and other complex instructions. Orca addresses challenges such as small scale homogeneous training data and a lack of rigorous evaluation by tapping into large-scale and complex reasoning benchmarks like Big-Bench Hard (BBH) and AGIEval.