QueryBandits for Hallucination Mitigation: Exploiting Semantic Features
  for No-Regret Rewriting
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.
68ae81c3364411bea07df81a
Nicole Cho
68ae81c3364411bea07df81b
William Watson
68ae81c3364411bea07df81c
Alec Koppel
68ae81c3364411bea07df81d
Sumitra Ganesh
68ae81c3364411bea07df81e
Manuela Veloso
QueryBandits for Hallucination Mitigation: Exploiting Semantic Features
  for No-Regret Rewriting
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.
QueryBandits, a bandit framework, effectively mitigates hallucinations in LLMs by proactively rewriting queries based on linguistic features, outperforming static prompting strategies.
QueryBandits bandit framework reward model hallucination propensity linguistic features query rewrite Thompson Sampling QA benchmarks cumulative regret per-arm regression semantic features forward-pass mechanisms