Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models

2025-04-17

AI Summary

Large language models (LLMs) frequently produce erroneous outputs on deterministic tasks—such as counting and abbreviation generation—due to interference from implicit prior distributions encoded in their parameters. This work identifies, for the first time, a localizable and controllable “prior-influence layer” within LLMs. We propose a synergistic approach integrating mechanistic interpretability analysis, layer-wise activation intervention, and lightweight fine-tuning: prompt engineering suppresses prior-induced bias, while targeted fine-tuning of only this layer effectively decouples model outputs from spurious prior correlations. Experiments demonstrate substantial performance gains on prior-dominated tasks; error rates become fully independent of prior probabilities, and ablation confirms that correct answers are already embedded in the original model’s internal representations. Our framework establishes a novel paradigm for diagnosing, interpreting, and correcting implicit priors in LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) sometimes fail to respond appropriately to deterministic tasks -- such as counting or forming acronyms -- because the implicit prior distribution they have learned over sequences of tokens influences their responses. In this work, we show that, in at least some cases, LLMs actually compute the information needed to perform these tasks correctly, and we identify some interventions that can allow them to access this information to improve their performance. First, we show that simply prompting the language model to not rely on its prior knowledge leads to dramatic improvements in prior-dominated tasks. We then use mechanistic interpretability techniques to localize the prior within the LLM and manipulate the extent to which that prior influences its responses. Specifically, we show that it is possible to identify layers of the underlying neural network that correlate with the prior probability of a response and that lightweight finetuning of these layers with basic prompts on prior-dominated tasks achieves high performance on held-out answers. These results suggest that the information required to produce a correct response is contained within the representations of the problems formed by the models. Furthermore, we show that this finetuning is significantly more effective for prior-dominated tasks, and that the error after finetuning is no longer correlated with the prior. Our results suggest that it may be possible to define effective methods for manipulating the extent to which LLMs rely upon their priors in solving problems, potentially increasing their performance in settings where LLMs hallucinate for reasons related to the prior probability of token sequences.

Problem

• Mitigate prior distribution influence in LLMs
• Improve deterministic task performance via interventions
• Localize and manipulate prior-related neural layers

Innovation

• Prompting LLMs to avoid prior knowledge reliance
• Localizing prior influence via mechanistic interpretability
• Lightweight finetuning targeted at prior-dominated layers