LLM Reasoning
Research on Large Language Model (LLM) reasoning focuses on improving the ability of LLMs to perform complex, multi-step reasoning tasks, often by augmenting them with techniques like chain-of-thought prompting, reinforcement learning (RL), and integration with symbolic reasoning methods. Current efforts concentrate on enhancing the accuracy and reliability of LLM reasoning, addressing issues like hallucination and inconsistent performance across different domains and tasks, often through improved credit assignment in RL and the development of novel evaluation metrics. These advancements are significant because reliable LLM reasoning is crucial for building trustworthy AI systems across diverse applications, from robotics and healthcare to scientific discovery and decision support.
Papers
Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent
Fatemeh Haji, Mazal Bethany, Maryam Tabar, Jason Chiang, Anthony Rios, Peyman Najafirad
BoViLA: Bootstrapping Video-Language Alignment via LLM-Based Self-Questioning and Answering
Jin Chen, Kaijing Ma, Haojian Huang, Jiayu Shen, Han Fang, Xianghao Zang, Chao Ban, Zhongjiang He, Hao Sun, Yanmei Kang
CoT Rerailer: Enhancing the Reliability of Large Language Models in Complex Reasoning Tasks through Error Detection and Correction
Guangya Wan, Yuqi Wu, Jie Chen, Sheng Li
LLMs are Superior Feedback Providers: Bootstrapping Reasoning for Lie Detection with Self-Generated Feedback
Tanushree Banerjee, Richard Zhu, Runzhe Yang, Karthik Narasimhan
CaLMQA: Exploring culturally specific long-form question answering across 23 languages
Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi
LLM-ARC: Enhancing LLMs with an Automated Reasoning Critic
Aditya Kalyanpur, Kailash Karthik Saravanakumar, Victor Barres, Jennifer Chu-Carroll, David Melville, David Ferrucci