most citedThought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20242 cited

Promoting Equality in Large Language Models: Identifying and Mitigating the Implicit Bias based on Bayesian Theory

Yongxin Deng, Xihe Qiu, Xiaoyu Tan +6

Large language models (LLMs) are trained on extensive text corpora, which inevitably include biased information. Although techniques such as Affective Alignment can mitigate some n…

cs.AI20244 cited

Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought

Xiaoyu Tan, Yongxin Deng, Xihe Qiu +5

Large language models (LLMs) have shown exceptional performance as general-purpose assistants, excelling across a variety of reasoning tasks. This achievement represents a signific…

cs.CL20241 cited

Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models

Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6

Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…

cs.CL20241 cited

Struct-X: Enhancing Large Language Models Reasoning with Structured Data

Xiaoyu Tan, Haoyu Wang, Xihe Qiu +4

Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs). Still, its integration poses a ch…

cs.RO2024

Subequivariant Reinforcement Learning Framework for Coordinated Motion Control

Haoyu Wang, Xiaoyu Tan, Xihe Qiu +1

Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods…