Summary Distilling Step-by-Step Outperforming Larger Language Models arxiv.org
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Distilling Step-by-Step is a more efficient and effective method for training smaller task-specific language models by using a distillation approach to extract rationales from larger language models, outperforming standard finetuning and distillation methods.
Key Points
- Distilling Step-by-Step is a new method for training smaller task-specific models that outperform larger language models (LLMs).
- The method involves extracting rationales from LLMs and using them to train smaller models with less training data and smaller model sizes.
- Distilling Step-by-Step consistently outperforms standard finetuning and task distillation methods across various NLP tasks and datasets.
- The approach is data-efficient, requires less computation cost for deployment, and improves model interpretability.
- The proposed method has been shown to close the performance gap between multilingual QA and reduce anti-social behaviors in LLMs.
Summaries
314 word summary
This paper presents a step-by-step approach to outperforming larger language models using several datasets, including CQA, ANLI, e-SNLI, ASDiv, and SVAMP. The authors randomly subsample 10% of each dataset and augment them with human-labeled explanations, then train T5-XXL (11B), T5-Base (220M), and T5-Large (770M) models with specific hyperparameters. The paper includes implementation and experiment details and references other relevant works. The document covers various techniques for improving the performance of large language models, including weighted distillation with unlabeled examples, language model fine-tuning for text classification, self-supervised models for semi-supervised learning, and model compression for more efficient training and deployment. The article proposes a technique called Distilling step-by-step to extract rationales from larger language models (LLMs) and use them as informative supervision in training smaller task-specific models. The method reduces the training dataset required to curate smaller models and can outperform the original LLM's performance. It can perform better than standard finetuning and distillation with less data and a smaller model. The article also discusses the potential for using Distilling step-by-step to improve multilingual QA and reduce anti-social behaviors in LLMs. Distilling Step-by-Step is a new method for natural language processing tasks that outperforms larger language models (LLMs) in terms of performance and data efficiency. It involves training a task-specific model by treating a teacher LLM's predicted labels as ground-truths. The approach consistently outperforms two common methods in learning task-specific models: standard finetuning and standard task distillation, even when using much less labeled and unlabeled data. Distilling Step-by-Step introduces a new paradigm for training smaller models that outperform LLMs, using a distillation approach to extract rationales from LLMs as informative task knowledge into training smaller task-specific models. This approach allows for efficient leveraging of additional unlabeled data to match LLM performance and reduce computation cost for deployment. The proposed method is data-efficient and has been shown to outperform larger language models on fully unlabeled datasets.
715 word summary
Distilling Step-by-Step introduces a new paradigm for training smaller models that outperform larger language models (LLMs). The method uses a distillation approach to extract rationales from LLMs as informative task knowledge into training smaller task-specific models, which reduces both the deployed model size as well as the data required for training. This approach allows for efficient leveraging of additional unlabeled data to match LLM performance and reduce computation cost for deployment. The document describes a framework for training smaller language models using generated rationales, which are natural language explanations for the model's predicted labels. The framework involves using CoT prompting to generate both an example input and rationale, which are used to train the larger language model (LLM). The LLM is then prompted to generate output labels and rationales for an unlabeled dataset, which are used to train smaller downstream models. The proposed method is data-efficient and has been shown to outperform larger language models on fully unlabeled datasets. The Distilling Step-by-Step approach is a new method for natural language processing tasks that outperforms larger language models (LLMs) in terms of performance and data efficiency. The method involves training a task-specific model by treating a teacher LLM's predicted labels as ground-truths. The authors compare Distilling step-by-step to two common methods in learning task-specific models: standard finetuning and standard task distillation, and show that it consistently outperforms the other two methods, even when using much less labeled and unlabeled data. The approach is able to achieve better performance than LLM's Few-shot CoT with a coarse-grained search and outperforms PaLM's Few-shot CoT with much smaller models by using less data. The method outperforms Few-shot CoT by using 2000x smaller models on e-SNLI and 45x smaller models on ANLI and CQA. Distilling step-by-step is able to much more efficiently exploit the value of added examples to achieve the same performance level of Few-shot task distillation. On SVAMP, by adding unlabeled examples from ASDiv, we close the gap to Few-shot CoT whereas Standard distillation still struggles to catch up. The approach is a promising new method for natural language processing tasks. The article proposes a technique called Distilling step-by-step to extract rationales from larger language models (LLMs) and use them as informative supervision in training smaller task-specific models. The method reduces the training dataset required to curate smaller models and can outperform the original LLM's performance. It can perform better than standard finetuning and distillation with less data and a smaller model. The article also discusses the potential for using Distilling step-by-step to improve multilingual QA and reduce anti-social behaviors in LLMs.
The document covers various techniques for improving the performance of large language models, including weighted distillation with unlabeled examples, language model fine-tuning for text classification, self-supervised models for semi-supervised learning, and model compression for more efficient training and deployment. Other topics include interpretable question-answering pipelines and the role of explanation data in model learning.
The text excerpt contains a list of references and authors related to language models and their performance, covering topics such as training models with explanations, evaluating model explanations, solving math word problems, reasoning, and distillation. The references also cover conferences such as the Association for Computational Linguistics and the Conference on Fairness, Accountability, and Transparency.
The document “Distilling Step-by-Step Outperforming Larger Language Models” is a compilation of various research papers in the field of natural language processing and machine learning, covering a range of topics including side-additive networks, bootstrapping reasoning with reasoning, using annotator rationales to improve machine learning, measuring association between labels and language models, eliciting reasoning in large models, faithful language reasoning using prompt-generated rationales, and distilling task-specific knowledge from BERT into simple neural networks. Other papers discuss language models for dialog applications, commonsense question answering, transfer learning with Jacobian matching, and training large-scale generative language models. This paper presents a step-by-step approach to outperforming larger language models. The authors use several datasets, including CQA, ANLI, e-SNLI, ASDiv, and SVAMP, and provide statistics for each in Table 1. They randomly subsample 10% of each dataset and augment them with human-labeled explanations. They train T5-XXL (11B), T5-Base (220M), and T5-Large (770M) models with specific hyperparameters using publicly available packages from huggingface/transformers. They perform their experiments on cloud A100x16 GPU instances. The paper includes implementation and experiment details and references other relevant works.
1393 word summary
The paper discusses a step-by-step approach for outperforming larger language models. The authors use several datasets, including CQA, ANLI, e-SNLI, ASDiv, and SVAMP, and provide statistics for each in Table 1. They randomly subsample 10% of each dataset and augment them with human-labeled explanations. They train T5-XXL (11B), T5-Base (220M), and T5-Large (770M) models with specific hyperparameters using publicly available packages from huggingface/transformers. They perform their experiments on cloud A100x16 GPU instances. The paper includes implementation and experiment details and references other relevant works. The document "Distilling Step-by-Step Outperforming Larger Language Models" is a compilation of various research papers in the field of natural language processing and machine learning. The papers cover a range of topics, including side-additive networks, bootstrapping reasoning with reasoning, using annotator rationales to improve machine learning, measuring association between labels and language models, eliciting reasoning in large models, faithful language reasoning using prompt-generated rationales, and distilling task-specific knowledge from BERT into simple neural networks. Other papers discuss language models for dialog applications, commonsense question answering, transfer learning with Jacobian matching, and training large-scale generative language models. The compilation includes research from various conferences and journals, such as the European Conference on Computer Vision, the Association for Computational Linguistics, and the Conference on Machine Learning. This text excerpt contains a list of references and authors related to language models and their performance. The references cover topics such as training models with explanations, evaluating model explanations, solving math word problems, reasoning, and distillation. The authors mentioned include Stephen H Bach, Ryan Smith, Jason A Fries, Braden Hancock, Michael C Hughes, Danish Pruthi, Colin Raffel, Noam Shazeer, Adina Williams, and Richard Socher, among others. The references also cover conferences such as the Association for Computational Linguistics and the Conference on Fairness, Accountability, and Transparency. Additionally, there are mentions of preprints and papers on zero-shot reasoners and large language models. This document discusses various techniques for improving the performance of large language models, including weighted distillation with unlabeled examples and language model fine-tuning for text classification. It also covers the use of self-supervised models for semi-supervised learning and the compression of models for more efficient training and deployment. Other topics include the creation of interpretable question-answering pipelines and the role of explanation data in model learning. The document references several relevant papers and studies on these topics. The article proposes a technique called Distilling step-by-step to extract rationales from larger language models (LLMs) and use them as informative supervision in training smaller task-specific models. The method reduces the training dataset required to curate smaller models and can outperform the original LLM's performance. The article notes that while Distilling step-by-step has limitations, it can perform better than standard finetuning and distillation with less data and a smaller model. The article also discusses the potential for using Distilling step-by-step to improve multilingual QA and reduce anti-social behaviors in LLMs. The article discusses a method called "Distilling step-by-step" that outperforms standard fine-tuning and larger language models (LLMs) using less data and smaller models. The method is able to achieve better performance than LLM's Few-shot CoT with a coarse-grained search and outperforms PaLM's Few-shot CoT with much smaller models by using less data. The results are visualized by plotting different results, under human-labeled and unlabeled settings. Distilling step-by-step is able to much more efficiently exploit the value of added examples to achieve the same performance level of Few-shot task distillation. The method outperforms Few-shot CoT by using 2000x smaller models on e-SNLI and 45x smaller models on ANLI and CQA. On SVAMP, by adding unlabeled examples from ASDiv, we close the gap to Few-shot CoT whereas Standard distillation still struggles to catch up. Standard finetuning fails to match LLM's performance using the same model size. The article presents experimental results comparing Distilling Step-by-Step (DSS) with standard finetuning and distillation methods for language models. DSS consistently outperforms standard methods across varying model sizes and tasks, achieving better Few-shot CoT and PINTO tuning on all four datasets considered. DSS requires much less unlabeled data to outperform standard task distillation, and can achieve better performance than larger language models such as PaLM with smaller T5 models. The article also proposes augmenting the relatively small number of data points in a dataset where the distilled model underperforms. The article discusses a method called Distilling Step-by-Step that outperforms larger language models in natural language processing tasks. The method involves training a task-specific model by treating a teacher LLM's predicted labels as ground-truths. The authors compare Distilling step-by-step to two common methods in learning task-specific models: standard finetuning and standard task distillation. They conduct experiments on four popular benchmark datasets across three different NLP tasks and show that Distilling step-by-step consistently outperforms the other two methods, even when using much less labeled and unlabeled data. The authors also investigate the minimum resources required for Distilling step-by-step to outperform LLMs and show that it can achieve the same performance with much smaller model size, reducing both the number of training examples and the deployment cost compared to LLMs. More dataset and implementation details are included in the appendices. The article presents a new approach called Distilling Step-by-Step that outperforms larger language models (LLMs) in terms of performance and data efficiency. The approach involves distillation by using only a small subset of the full unlabeled dataset and generating intermediate reasoning steps to guide the model in predicting the resultant label. The smaller model is trained to not only predict task labels but also generate corresponding rationales. The approach is compared to standard finetuning and task distillation and is shown to be more effective. Overall, the Distilling Step-by-Step approach is a promising new method for natural language processing tasks. The document describes a framework for training smaller language models using rationales, which are natural language explanations for the model's predicted labels. The framework involves using CoT prompting to generate both an example input and rationale, which are used to train the larger language model (LLM). The LLM is then prompted to generate output labels and rationales for an unlabeled dataset, which are used to train smaller downstream models. The proposed method is data-efficient and has been shown to outperform larger language models on fully unlabeled datasets. The effectiveness of the method has been demonstrated through various experiments. This text excerpt discusses a new approach to training smaller task-specific models using generated rationales from larger language models (LLMs). The authors propose a framework where task prefixes are added to input examples, and the model is trained to output differently based on the prefix. Generated rationales are then used to train small task-specific models in a multi-task learning setting. The use of generated rationales can reduce the need for large amounts of labeled data and improve model interpretability. The authors compare their approach to other recent knowledge distillation research and propose future investigations into using both human-generated and LLM-generated rationales. Distilling Step-by-Step uses a distillation approach to distill the capabilities of larger language models (LLMs) into smaller models. The method allows for efficient leveraging of additional unlabeled data to match LLM performance. Smaller models using this method outperform LLMs and require less data and computation cost for deployment. The distillation approach reduces model size and simultaneously learns task-specific smaller models that can reason with chain-of-thought (CoT) reasoning. The method is a new mechanism for training smaller models with less training data that outperform LLMs. Distilling Step-by-Step proposes a new paradigm for training smaller models that outperform larger language models (LLMs) and require less memory and compute. LLMs are challenging to deploy in real-world applications due to their sheer size and the amount of data required for training. To circumvent these challenges, practitioners often choose to deploy applications that require low latency performance. However, such computational requirements are far beyond affordable for most product teams, especially for applications that require strong zero/few-shot performance. Distilling step-by-step extracts rationales from LLMs as informative task knowledge into training smaller task-specific models, which reduces both the deployed model size as well as the data required for training. Compared to LLMs, Distilling Step-by-Step achieves better performance using substantially smaller model sizes and much fewer labeled/unlabeled training examples. It introduces a new mechanism that trains smaller models by leveraging less training data needed to achieve comparable performance to finetuning or distillation, which require large amounts of training data to achieve better performance with generated labels.