activity
20222026
most citedRLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Self-Evolving LLM Memory Extraction Across Heterogeneous Tasks

Yuqing Yang, Tengxiao Liu, Wang Bill Zhu +3

As LLM-based assistants become persistent and personalized, they must extract and retain useful information from past conversations as memory. However, the types of information wor…

cs.AI2026

WildSci: Advancing Scientific Reasoning from In-the-Wild Literature

Tengxiao Liu, Deepak Nathani, Zekun Li +2

Recent progress in large language model (LLM) reasoning has focused on domains like mathematics and coding, where abundant high-quality data and objective evaluation metrics are re…

cs.CL2024

Can Language Models Learn to Skip Steps?

Tengxiao Liu, Qipeng Guo, Xiangkun Hu +4

Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intrig…

cs.CL2024

ECon: On the Detection and Resolution of Evidence Conflicts

Cheng Jiayang, Chunkit Chan, Qianqian Zhuang +7

The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and c…

cs.CL20222 cited

RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

Tengxiao Liu, Qipeng Guo, Xiangkun Hu +3

Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, mana…