activity
20242026
most citedFact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2026

A Practical Framework for Flaky Failure Triage in Distributed Database Continuous Integration

Jun-Peng Zhu, Qizhi Wang, Yulong Zhai +8

Flaky failure triage is crucial for keeping distributed database continuous integration (CI) efficient and reliable. After a failure is observed, operators must quickly decide whet…

cs.IR2025

Revela: Dense Retriever Learning via Language Modeling

Fengyu Cai, Tong Chen, Xinran Zhao +5

Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-do…

cs.CL2024

: Towards Measuring Class-wise Hardness through Modelling Class Semantics

Fengyu Cai, Xinran Zhao, Hongming Zhang +2

Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. However, class-specific properties are overlo…

cs.IR2024

: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity

Fengyu Cai, Xinran Zhao, Tong Chen +4

Recent studies show the growing significance of document retrieval in the generation of LLMs, i.e., RAG, within the scientific domain by bridging their knowledge gap. However, dens…

cs.IR2024

Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness

Xinran Zhao, Tong Chen, Sihao Chen +2

The task of Information Retrieval (IR) requires a system to identify relevant documents based on users' information needs. In real-world scenarios, retrievers are expected to not o…

cs.CL2024★ 1 cited

Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Xinran Zhao, Hongming Zhang, Xiaoman Pan +4

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by…