复杂推理 Top15 被引盘点
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复杂推理 Top15 被引盘点
复杂推理 Top15 被引盘点
飞书用户27492024年11月30日修改
本文精选了 EMNLP 2024 论文集中与复杂推理相关的、被引量最高的15篇论文*。后续我们还会继续陆续发布不同领域的 EMNLP 2024 高引盘点，在机智流公众号后台对话框回复“盘点”，加入顶会论文盘点交流群。
*注：引用数据来自谷歌学术，数据统计截止 2024 年 11 月 13 日
1.
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate（215次）
2.
Zero-Resource Hallucination Prevention for Large Language Models（30次）
3.
Android in the Zoo: Chain-of-Action-Thought for GUI Agents（18次）
4.
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities（11次）
5.
Puzzle Solving using Reasoning of Large Language Models: A Survey（10次）
6.
Few shot chain-of-thought driven reasoning to prompt LLMs for open-ended medical question answering（10次）
7.
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models（10次）
8.
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing（7次）
9.
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs（7次）
10.
Divide-or-Conquer? Which Part Should You Distill Your LLM?（7次）
11.
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models（5次）
12.
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models（4次）
13.
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning（4次）
14.
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning（4次）
15.
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning（3次）
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
现代大型语言模型（LLMs）如ChatGPT在一般语言任务上表现出色，但在复杂推理任务上仍存在困难，这推动了对LLMs认知行为的研究以探索类人解题策略。自我反思是一种代表性策略，但存在思维退化（DoT）问题，即一旦LLM对其解决方案建立信心，即使初始立场错误，后续也无法通过反思产生新想法。为解决该问题，提出多智能体辩论（MAD）框架，多个智能体“针锋相对”地表达论点，由裁判管理辩论过程以获得最终解决方案，该框架鼓励LLMs的发散性思维，有助于需要深度思考的任务。在两个具有挑战性的数据集上的实验结果证明了MAD框架的有效性，分析表明MAD要取得良好性能需要自适应地中断辩论和适度的“针锋相对”状态，还发现若智能体使用不同LLM，LLM可能不是公平的裁判，代码可在指定网址获取。
Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self-reflection, which asks an LLM to refine the solution with the feedback generated by itself iteratively. However, our study shows that such reflection-style methods suffer from the Degeneration-of-Thought (DoT) problem: once the LLM has established confidence in its solutions, it is unable to generate novel thoughts later through reflection even if its initial stance is incorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution. Clearly, our MAD framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation. Experiment results on two challenging datasets, commonsense machine translation and counter-intuitive arithmetic reasoning, demonstrate the effectiveness of our MAD framework. Extensive analyses suggest that the adaptive break of debate and the modest level of "tit for tat" state are required for MAD to obtain good performance. Moreover, we find that LLMs might not be a fair judge if different LLMs are used for agents. Code is available at this https URL.
Zero-Resource Hallucination Prevention for Large Language Models