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20242026
most citedDecoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2026

TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG

Tianhua Zhang, Kun Li, Junan Li +5

Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced b…

cs.CL2025

ROVER: Recursive Reasoning Over Videos with Vision-Language Models for Embodied Tasks

Philip Schroeder, Ondrej Biza, Thomas Weng +2

Vision-language models (VLMs) have exhibited impressive capabilities across diverse image understanding tasks, but still struggle in settings that require reasoning over extended s…

cs.CL2025

RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning

Kun Li, Yunxiang Li, Tianhua Zhang +4

Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directl…

cs.CL2025

Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution

Kun Li, Tianhua Zhang, Yunxiang Li +5

Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in…

cs.CL2024★ 1 cited

Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains

Kun Li, Tianhua Zhang, Xixin Wu +3

Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) due to their structured representation of knowledge. Existing research on the utilization…

cs.CL2024

Quantifying Generalization Complexity for Large Language Models

Zhenting Qi, Hongyin Luo, Xuliang Huang +5

While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often…

cs.CL2024

Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers

Tianhua Zhang, Kun Li, Hongyin Luo +3

Query rewriting is a crucial technique for passage retrieval in open-domain conversational question answering (CQA). It decontexualizes conversational queries into self-contained q…

cs.CL2024

THREAD: Thinking Deeper with Recursive Spawning

Philip Schroeder, Nathaniel Morgan, Hongyin Luo +1

Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this c…