Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of existing work mitigates hallucination through detection or abstention mechanisms, but leaves open how models internally arrive at the decis...
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 0 citations
LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, violating the information-access boundary of pre-search users. Existing validation metrics, including overlap, diversity, and effectiveness, cannot distinguish rare human-...
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi et al.· 0 citations
Twin Worlds (TW), a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention, is proposed, which identifies when answers are not reliably grounded in the provided evidence and outperforms uncertainty- and sufficiency-based baselines.
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 1 citation
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