No One Size Fits All: QueryBandits for Hallucination Mitigation
arXiv:2602.20332v1 Announce Type: new Abstract: Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detection and parameter editing. The dearth of studies focusing on hallucinations in closed-source models is especially concerning, as they constitute the vast majority of models in institutional deployments. We introduce QueryBandits, a model-agnostic contextual bandit framework that adaptively learns online to select the optimal query-rewrite strategy by leveraging an empirically validated and calibrated reward function. Across 16 QA scenarios, our top QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a No-Rewrite baseline and outperforms zero-shot static policies (e.g., Paraphrase or Expand) by 42.6% and 60.3%, respectively. Moreover, all contextual bandits outperform vanilla bandits across all datasets, with higher feature variance coinciding with g
arXiv:2602.20332v1 Announce Type: new Abstract: Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detection and parameter editing. The dearth of studies focusing on hallucinations in closed-source models is especially concerning, as they constitute the vast majority of models in institutional deployments. We introduce QueryBandits, a model-agnostic contextual bandit framework that adaptively learns online to select the optimal query-rewrite strategy by leveraging an empirically validated and calibrated reward function. Across 16 QA scenarios, our top QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a No-Rewrite baseline and outperforms zero-shot static policies (e.g., Paraphrase or Expand) by 42.6% and 60.3%, respectively. Moreover, all contextual bandits outperform vanilla bandits across all datasets, with higher feature variance coinciding with greater variance in arm selection. This substantiates our finding that there is no single rewrite policy optimal for all queries. We also discover that certain static policies incur higher cumulative regret than No-Rewrite, indicating that an inflexible query-rewriting policy can worsen hallucinations. Thus, learning an online policy over semantic features with QueryBandits can shift model behavior purely through forward-pass mechanisms, enabling its use with closed-source models and bypassing the need for retraining or gradient-based adaptation.
Executive Summary
The article 'No One Size Fits All: QueryBandits for Hallucination Mitigation' addresses the critical issue of hallucinations in Large Language Models (LLMs), particularly focusing on closed-source models which are widely used in institutional settings. The authors introduce QueryBandits, a model-agnostic contextual bandit framework that adaptively learns to select the optimal query-rewrite strategy using a calibrated reward function. Through extensive testing across 16 QA scenarios, the top-performing QueryBandit (Thompson Sampling) achieved an 87.5% win rate over a No-Rewrite baseline and significantly outperformed static policies. The study highlights the importance of adaptive, context-aware strategies in mitigating hallucinations, as inflexible policies can exacerbate the problem.
Key Points
- ▸ Introduction of QueryBandits, a model-agnostic framework for hallucination mitigation in LLMs.
- ▸ Significant performance improvement over static policies and No-Rewrite baselines.
- ▸ Adaptive learning through contextual bandits is crucial for effective hallucination mitigation.
- ▸ Inflexible query-rewriting policies can worsen hallucinations.
- ▸ QueryBandits can be applied to closed-source models without retraining or gradient-based adaptation.
Merits
Innovative Framework
QueryBandits presents a novel approach to hallucination mitigation that is model-agnostic and adaptable, making it suitable for a wide range of applications.
Empirical Validation
The study provides robust empirical evidence supporting the effectiveness of QueryBandits through extensive testing across multiple QA scenarios.
Practical Applicability
The framework's ability to work with closed-source models without the need for retraining or gradient-based adaptation enhances its practical utility.
Demerits
Limited Scope
The study focuses primarily on QA scenarios, which may not fully capture the breadth of hallucination issues across different types of tasks and models.
Complexity
The implementation and tuning of QueryBandits may require significant expertise and resources, potentially limiting its accessibility.
Generalizability
While the results are promising, further research is needed to validate the framework's effectiveness across a broader range of models and applications.
Expert Commentary
The article presents a significant advancement in the field of hallucination mitigation for LLMs, particularly in the context of closed-source models. The introduction of QueryBandits offers a practical and adaptable solution that addresses a critical gap in the current literature. The empirical validation across multiple QA scenarios provides strong evidence of its effectiveness, and the model-agnostic nature of the framework enhances its applicability. However, the study's focus on QA scenarios and the potential complexity of implementation are notable limitations. Further research is needed to explore the generalizability of QueryBandits across different types of tasks and models. The practical implications for organizations using closed-source LLMs are substantial, as the framework can improve the accuracy and reliability of AI systems without the need for extensive retraining. From a policy perspective, the study highlights the importance of adaptive AI frameworks in the governance and regulation of AI technologies, emphasizing the need for continued research and development in this area.
Recommendations
- ✓ Conduct further research to validate the effectiveness of QueryBandits across a broader range of tasks and models.
- ✓ Explore the integration of QueryBandits into existing AI workflows to enhance performance and reduce the risk of hallucinations.
