Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Multi-LLM collaboration detects knowledge gaps to make LLMs abstain from wrong answers instead of hallucinating.
Published 202437 citationsPaper ↗
72%
OverallHighly rated
?
OverallHighly ratedVote to see the scoreThe exact score shows once you've voted, so every vote is your own call. The first half of each home page shelf shows its scores.
Readers
–
Only vote on papers you've read. Sign in with GitHub to vote.
AI panel8/21reviewers recommend it
lenient 5/5
medium 3/11
strict 0/5
AI panel?Vote to see what the 20 AI reviewers said
Multi-LLM collaboration advances selective abstention by closing calibrated single-model gaps, though unpriced compute overhead and missing standard benchmarks leave its gains unverified.
Abstract
Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, Yulia Tsvetkov. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.