Summary Tree of Uncertain Thoughts Reasoning for Large Language Models arxiv.org
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The Tree of Uncertain Thoughts (TouT) is a framework that improves the reasoning abilities of Large Language Models (LLMs).
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Key Points
- The Tree of Uncertain Thoughts (TouT) is a reasoning framework tailored for Large Language Models (LLMs).
- TouT leverages Monte Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse local responses.
- By integrating local uncertainty quantification with global search algorithms, TouT enhances the accuracy of model responses.
- TouT outperforms the Tree of Thoughts (ToT) and chain-of-thought prompting methods in rigorous experiments on planning tasks.
- Large Language Models (LLMs) have shown remarkable prowess in tasks that demand reasoning, but their reasoning process primarily relies on autoregressive mechanisms.
Summaries
20 word summary
The Tree of Uncertain Thoughts (TouT) is a reasoning framework for Large Language Models (LLMs) that enhances their reasoning capabilities.
34 word summary
The Tree of Uncertain Thoughts (TouT) is a reasoning framework for Large Language Models (LLMs) that addresses local uncertainties. It proposes a thoughts reasoning framework for LLaMA-2 that enhances the reasoning capabilities of LLMs.
249 word summary
The Tree of Uncertain Thoughts (TouT) is a reasoning framework designed for Large Language Models (LLMs) that addresses the local uncertainties in intermediate decision points. These uncertainties arise from the potential for diverse responses in LLMs and can impact
LLaMA-2 focused on the intersection of linguistic properties and deep learning capabilities in LLMs. The main focus of this work is to propose a thoughts reasoning framework for LLaMA-2 that enhances the reasoning capabilities of LLMs.
This excerpt discusses the use of uncertainty-aware inference in large language models (LLMs). The authors propose a method to explicitly quantify uncertainty for each local response in intermediate steps. They introduce a novel uncertainty evaluator that generates a confidence score for each local intermediate state
We conducted experiments using the Breadth-first search algorithm to test the success rate of Mini Crosswords games. We compared our results to previous methods and used the same LLM weight for a fair comparison. Our experiments were conducted on NVIDIA-A100 GPUs.
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The effectiveness of using Large
This excerpt provides a list of references to related research on large language models. The references include papers and preprints that explore various aspects of language models, such as their ability to multitask, their few-shot learning capabilities, and techniques for improving their reasoning