2 citations · 6 across the 39 of their papers we have counts for
10 papers · 1 filter
Shared Experience, Separate Learning: Companion Confidence Calibration for LLMs
Shiyu Ni, Keping Bi, Jiafeng Guo +4
Reliable self-assessment is essential for large language models (LLMs), yet they often remain highly confident when their answers are wrong. We study \emph{concurrent confidence ca…
Can LLM Rerankers Predict Their Own Ranking Performance?
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction…
Beyond Relevance: Utility-Centric Retrieval in the LLM Era
Hengran Zhang, Minghao Tang, Keping Bi +1
Information retrieval systems have traditionally optimized for topical relevance-the degree to which retrieved documents match a query. However, relevance only approximates a deepe…
Relevance Supervision Can Teach Neural Retrievers to Prefer LLM-Generated Text
Wei Huang, Keping Bi, Yinqiong Cai +3
Recent studies show that neural retrievers often exhibit source bias, ranking LLM-generated passages above semantically similar human-written ones, which threatens the reliability…
How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality
Minzhu Tu, Shiyu Ni, Keping Bi
Large language models (LLMs) has been widely adopted as a scalable surrogate for human evaluation, yet such judges remain imperfect and susceptible to surface-level biases. One pos…
AuthorityBench: Benchmarking LLM Authority Perception for Reliable Retrieval-Augmented Generation
Zhihui Yao, Hengran Zhang, Keping Bi
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with external knowledge but remains vulnerable to low-authority sources that can propagate misinformation…
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
Jiahan Chen, Da Li, Hengran Zhang +6
Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classi…
Bagging-Based Model Merging for Robust General Text Embeddings
Hengran Zhang, Keping Bi, Jiafeng Guo +4
General-purpose text embedding models underpin a wide range of NLP and information retrieval applications, and are typically trained on large-scale multi-task corpora to encourage…
Training Dense Retrievers with Multiple Positive Passages
Benben Wang, Minghao Tang, Hengran Zhang +2
Modern knowledge-intensive systems, such as retrieval-augmented generation (RAG), rely on effective retrievers to establish the performance ceiling for downstream modules. However,…
Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
Yuhan Wang, Shiyu Ni, Zhikai Ding +3
Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied under single-answer question an…