6 papers
CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models
Yu Fu, Yongqi Kang, Yong Zhao
Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the sup…
X-MADAM-RAG: Diagnosing and Handling Chinese-English Evidence Conflict in Retrieval-Augmented Generation
Yongqi Kang, Yu Fu, Yong Zhao
Retrieval-augmented generation (RAG) systems may receive evidence that is not merely noisy but mutually contradictory. This issue becomes particularly salient in multilingual setti…
ECHO: Explainable Co-editing with Human-in-the-loop Operations for Presentation Refinement
Yu Fu, Yongqi Kang, Yujia Zhou +1
Authoring and refining presentation slides is a highly time-consuming core task in academic and business domains. While generative AI tools have lowered the barrier for creating in…
Knowledge Boundary of Large Language Models: A Survey
Moxin Li, Yong Zhao, Wenxuan Zhang +5
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, lead…
Aligning Large Language Models for Faithful Integrity Against Opposing Argument
Yong Zhao, Yang Deng, See-Kiong Ng +1
Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations,…
Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
Yang Deng, Yong Zhao, Moxin Li +2
Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have…