6 papers · 1 filter
A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization
Weixiao Zhou, Gengyao Li, Xianfu Cheng +3
Despite the significant advancement of LLMs in conversation summarization, their evaluation remains limited by insufficient scenarios, input lengths, and sample sizes. Furthermore,…
GenProve: Learning to Generate Text with Fine-Grained Provenance
Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao +5
Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a c…
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering
Junnan Zhu, Jingyi Wang, Bohan Yu +4
LLMs have shown impressive progress in natural language processing. However, they still face significant challenges in TableQA, where real-world complexities such as diverse table…
Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains
Peiran Zhou, Junnan Zhu, Yichen Shen +1
Large Language Models (LLMs) excel in language tasks but are prone to hallucinations and outdated knowledge. Retrieval-Augmented Generation (RAG) mitigates these by grounding LLMs…
ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering
Caijun Jia, Nan Xu, Jingxuan Wei +4
Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reason…
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Jingxuan Wei, Nan Xu, Junnan Zhu +4
Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promisin…