Summary Graph of Thoughts Solving Elaborate Problems arxiv.org
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The Graph of Thoughts framework improves large language models by representing thoughts as a graph and leveraging feedback to combine and enhance them.
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Slide Presentation (12 slides)
Key Points
- The Graph of Thoughts (GoT) framework enhances the prompting capabilities of large language models (LLMs) by modeling LLM thoughts as an arbitrary graph.
- GoT is a prompting scheme that outperforms other schemes and can be used for tasks that can be broken down into smaller subtasks.
- The GoT framework involves a thought generator and a state evaluator to solve complex problems.
- GoT allows for the seamless incorporation of reasoning schemes and enables the aggregation of thoughts to combine advantages and eliminate disadvantages.
- GoT offers extensible APIs for different prompting schemes and has use cases in sorting and merging lists.
- Clipping min(error-scope, n) is applied for clarity in plots, and a positive score is used to measure correctly computed elements.
- GoT consistently improves the quality of outcomes compared to other models like GPT-3.5 and Llama-2.
- The document includes references to various research papers and articles related to graph theory, language models, and problem-solving.
Summaries
25 word summary
The Graph of Thoughts (GoT) framework enhances large language models (LLMs) by modeling thoughts as a graph, allowing for combining and enhancing them with feedback.
39 word summary
The Graph of Thoughts (GoT) framework is introduced as a way to enhance the prompting capabilities of large language models (LLMs). It models LLM thoughts as a graph, allowing for combining thoughts and enhancing them using feedback. GoT outper
531 word summary
The paper introduces the Graph of Thoughts (GoT) framework, which enhances the prompting capabilities of large language models (LLMs) by modeling LLM thoughts as an arbitrary graph. This allows for combining thoughts into synergistic outcomes and enhancing thoughts using feedback
The Graph of Thoughts (GoT) is a prompting scheme that is well-suited for tasks that can be broken down into smaller subtasks and solved individually. It outperforms other schemes, improving upon CoT and ToT by 70%
The GoT framework is a graph-based model for solving complex problems. It involves a thought generator that creates new nodes based on a given node, and a state evaluator that assigns scores to each new node. The tree extension schedule is determined by the search
The Graph of Thoughts (GoT) framework allows for the seamless incorporation of reasoning schemes that remove unnecessary parts to save space. The specific form of the framework depends on a transformation. GoT enables the aggregation of thoughts to combine advantages and eliminate disadvantages.
GoO is a static structure that is constructed before execution and maintains updated information about the LLM reasoning process. It offers extensible APIs for different prompting schemes. Several use cases of GoT are described, including sorting and merging lists. The Graph of
The excerpt describes a prompt used in the Graph of Thoughts (GoT) system to solve a sorting problem. The prompt provides instructions on how to fix an incorrectly sorted list by comparing the frequency of numbers in the input list and the incorrectly sorted list.
To improve clarity in plots, a clipping min(error-scope, n) is applied when plotting the score, as some baselines result in outliers. A "positive score" can be used to describe the scope of correctly sorted elements, which is calculated
The excerpt discusses the Graph of Thoughts (GoT) method for solving complex problems. It explains the calculation of error-scope and the use of a positive score to measure correctly computed elements. The text also mentions keyword counting, which involves counting the frequency
In the study on solving elaborate problems using Graph of Thoughts (GoT), the researchers compared the performance of different models, including GPT-3.5 and Llama-2. They found that GoT consistently improved the quality of outcomes compared to
The document discusses the Graph of Thoughts (GoT), a new paradigm that enhances the capabilities of large language models (LLMs) without the need for model updates. GoT models LLM reasoning as a graph, where thoughts are vertices and dependencies are
This text excerpt consists of a list of references to various research papers and conference proceedings related to graph theory, language models, and problem-solving. The references include papers on topics such as teaching language models to self-debug, fast graph pattern matching, scaling language
The summary of the document "Graph of Thoughts Solving Elaborate Problems" includes a list of references to various research papers and articles. These references cover topics such as optimizing continuous prompts for generation, large language models, challenges in parallel graph processing,
The excerpted text includes references to various research papers and figures related to the topic of graph of thoughts and solving elaborate problems. The mentioned papers discuss topics such as open foundation and fine-tuned chat models, attention-based models, plan-and-solve prompting