While there have been extensive studies in code generation by large language
models (LLM), where benchmarks like HumanEval have been surpassed with an
impressive 96.3% success rate, these benchmarks predominantly judge a model's
performance on basic function-level code generation and lack the critical
thinking and concept of scope required of real-world scenarios such as solving
GitHub issues. This research introduces the application of the Tree of Thoughts
(ToT) language model reasoning framework for enhancing the decision-making and
problem-solving abilities of LLMs for this complex task. Compared to
traditional input-output (IO) prompting and Retrieval Augmented Generation
(RAG) techniques, ToT is designed to improve performance by facilitating a
structured exploration of multiple reasoning trajectories and enabling
self-assessment of potential solutions. We experimentally deploy ToT in
tackling a Github issue contained within an instance of the SWE-bench. However,
our results reveal that the ToT framework alone is not enough to give LLMs the
critical reasoning capabilities to outperform existing methods. In this paper
we analyze the potential causes of these shortcomings and identify key areas
for improvement such as deepening the thought process and introducing agentic
capabilities. The insights of this research are aimed at informing future
directions for refining the application of ToT and better harnessing the
potential of LLMs in real-world problem-solving scenarios.

本研究介绍了 Tree of Thoughts（ToT）语言模型推理框架的应用，以增强大型语言模型（LLM）在解决实际问题时的决策和问题解决能力。通过改进性能，促进多个推理轨迹的结构化探索和潜在解决方案的自我评估，ToT 框架被用于提高代码生成中复杂任务的性能。然而，研究结果显示，仅依靠 ToT 框架还不足以超越现有方法，并且需要加深思维过程和引入主体能力等方面进行改进。该研究的见解旨在为完善 ToT 的应用和更好地利用 LLMs 在实际问题解决场景中的潜力提供指导。

能用思维树解决 Github 问题吗？

Can Github issues be solved with Tree Of Thoughts?

While the recently introduced Tree of Thoughts (ToT) has heralded
advancements in allowing Large Language Models (LLMs) to reason through
foresight and backtracking for global decision-making, it has overlooked the
inherent local uncertainties in intermediate decision points or "thoughts".
These local uncertainties, intrinsic to LLMs given their potential for diverse
responses, remain a significant concern in the reasoning process. Addressing
this pivotal gap, we introduce the Tree of Uncertain Thoughts (TouT) - a
reasoning framework tailored for LLMs. Our TouT effectively leverages Monte
Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse
local responses at these intermediate steps. By marrying this local uncertainty
quantification with global search algorithms, TouT enhances the model's
precision in response generation. We substantiate our approach with rigorous
experiments on two demanding planning tasks: Game of 24 and Mini Crosswords.
The empirical evidence underscores TouT's superiority over both ToT and
chain-of-thought prompting methods.

我们引入了一种针对大型语言模型的推理框架 —— 不确定性思维树（TouT），通过有效利用蒙特卡洛丢弃法来量化这些中间步骤的多样化本地响应的不确定性评分，并将其与全局搜索算法结合起来，提高模型在响应生成方面的精确性。通过在两个具有挑战性的规划任务上进行严格实验，我们的实证证据突显了 TouT 在 ToT 和思维链提示方法上的优势。

大型语言模型的不确定思考推理树

Tree of Uncertain Thoughts Reasoning for Large Language Models

Language models are increasingly being deployed for general problem solving
across a wide range of tasks, but are still confined to token-level,
left-to-right decision-making processes during inference. This means they can
fall short in tasks that require exploration, strategic lookahead, or where
initial decisions play a pivotal role. To surmount these challenges, we
introduce a new framework for language model inference, Tree of Thoughts (ToT),
which generalizes over the popular Chain of Thought approach to prompting
language models, and enables exploration over coherent units of text (thoughts)
that serve as intermediate steps toward problem solving. ToT allows LMs to
perform deliberate decision making by considering multiple different reasoning
paths and self-evaluating choices to decide the next course of action, as well
as looking ahead or backtracking when necessary to make global choices. Our
experiments show that ToT significantly enhances language models'
problem-solving abilities on three novel tasks requiring non-trivial planning
or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in
Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of
tasks, our method achieved a success rate of 74%. Code repo with all prompts:
this https URL

通过 Tree of Thoughts (ToT) 框架，可以弥补语言模型在考虑多种不同推理路径时的局限性，进而提高语言模型在需要问题解决的任务中的表现。实验表明，使用 ToT 的方法大大提高了语言模型在三个需要非平凡计划或搜索的任务的解决能力：24 点游戏、创意写作和小型填字游戏。