QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting

TL;DR

This paper introduces QueryBandits, a bandit-based framework that proactively rewrites queries to reduce hallucinations in Large Language Models by leveraging semantic features, outperforming static prompts and no-rewrite baselines.

Contribution

Proposes a novel bandit approach for query rewriting that effectively mitigates hallucinations in LLMs by exploiting semantic features, without retraining or gradient updates.

Findings

QueryBandits achieves an 87.5% win rate over no-rewrite baseline.

Static prompts can worsen hallucinations compared to no-rewrite.

Rewrites guided by semantic features induce significant output shifts.

Abstract

Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather than shaping the queries that trigger them. We introduce QueryBandits, a bandit framework that designs rewrite strategies to maximize a reward model, that encapsulates hallucination propensity based upon the sensitivities of 17 linguistic features of the input query-and therefore, proactively steer LLMs away from generating hallucinations. Across 13 diverse QA benchmarks and 1,050 lexically perturbed queries per dataset, our top contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a no-rewrite baseline and also outperforms zero-shot static prompting ("paraphrase" or "expand") by 42.6% and 60.3% respectively. Therefore, we empirically substantiate the effectiveness of QueryBandits in mitigating hallucination via the intervention that takes the form of a query rewrite. Interestingly, certain static prompting strategies, which constitute a considerable number of current query rewriting literature, have a higher cumulative regret than the no-rewrite baseline, signifying that static rewrites can worsen hallucination. Moreover, we discover that the converged per-arm regression feature weight vectors substantiate that there is no single rewrite strategy optimal for all queries. In this context, guided rewriting via exploiting semantic features with QueryBandits can induce significant shifts in output behavior through forward-pass mechanisms, bypassing the need for retraining or gradient-based adaptation.