activity
20192026
most citedLLMaAA: Making Large Language Models as Active Annotators

3 citations · 8 across the 13 of their papers we have counts for

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cs.CL2026

Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph

Jianpeng Hu, Yanzeng Li, Jialun Zhong +2

The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithful…

cs.CL2025

SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

Hao Wang, Jialun Zhong, Changcheng Wang +5

Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical…

cs.CL2025

GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendation

Jialun Zhong, Yanzeng Li, Sen Hu +3

Medication recommendations have become an important task in the healthcare domain, especially in measuring the accuracy and safety of medical dialogue systems (MDS). Different from…

cs.CL2025

A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Jialun Zhong, Wei Shen, Yanzeng Li +7

Reward Model (RM) has demonstrated impressive potential for enhancing Large Language Models (LLM), as RM can serve as a proxy for human preferences, providing signals to guide LLMs…

cs.CL20241 cited

MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph

Xuan Yi, Yanzeng Li, Lei Zou

Multi-modal knowledge graphs have emerged as a powerful approach for information representation, combining data from different modalities such as text, images, and videos. While se…

cs.CL20233 cited

LLMaAA: Making Large Language Models as Active Annotators

Ruoyu Zhang, Yanzeng Li, Yongliang Ma +2

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acqu…