Context Copying Modulation: The Role of Entropy Neurons in Managing Parametric and Contextual Knowledge Conflicts (2025)

Abstract: The behavior of Large Language Models (LLMs) when facing contextual information that conflicts with their internal parametric knowledge is inconsistent, with no generally accepted explanation for the expected outcome distribution. Recent work has identified in autoregressive transformer models a class of neurons – called \textit{entropy neurons} – that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. In this paper, we investigate the preliminary claim that these neurons are involved in inhibiting context copying behavior in transformers by looking at their role in resolving conflicts between contextual and parametric information. We show that \textit{entropy neurons} are responsible for suppressing context copying across a range of LLMs, and that ablating them leads to a significant change in the generation process. These results enhance our understanding of the internal dynamics of LLMs when handling conflicting information.

## introduction large language models (llms) exhibit remarkable proficiency in representing, memorizing, and retrieving vast amounts of information. however, they often struggle when discrepancies arise between their learned parametric knowledge (pk) and the contextual knowledge (ck) provided at inference @xcite @xcite . these conflicts can lead to unpredictable model behavior, which poses a significant challenge in real-world applications @xcite . although various strategies have been proposed to mitigate this unpredictable behavior @xcite al., paris is the capital of italy. paris is the capital of ___ france italy entropy neurons parametric knowledge context knowledge . . . . . . 2024), the mechanisms that govern how llms prioritize and integrate different sources of knowledge are poorly understood. understanding these mechanisms is crucial, as the resolution of pk-ck conflicts directly impacts context-intensive tasks such as retrieval augmented generation (rag) and other applications where accuracy depends on balancing internal knowledge with external context. without a clear regulation process, models may either ignore reliable contextual cues or override their own parametric knowledge inappropriately. we investigate the preliminary claim that the recently discovered entropy neurons @xcite are involved in inhibiting context copying behavior @xcite ) by looking at their role in resolving conflicts between ck and pk. these neurons are known to regulate model entropy while having an overall understanding this balance mechanism is critical for developing more reliable and grounded language models. by elucidating how entropy neurons mediate these conflicts, we establish empirical grounds for targeted interventions that could enforce more consistent knowledge integration. this mechanistic understanding enables the development of safer models with reduced propensity for extrinsic and intrinsic hallucinations @xcite . we make the following key findings and contributions: ## related work the understanding of the mechanisms and knowledge localization within transformers has advanced through various studies. one line of research has focused on the pk-based outputs, particularly in factual settings @xcite @xcite @xcite . these studies hypothesized that llms store parametric information within the feed forward network (ffn) layers, which function as a key-value memory. this stored information is subsequently accessed by the multi-head self-attention (mhsa) layers. another body of work has examined ck-based outputs. these studies concluded that the processing of ck, unlike pk, is not localized within the llm's parameters @xcite . instead, it is facilitated by a learned mechanism known as induction, which underpins in-context learning and information copying @xcite . despite these advancements, the mechanisms underlying how llms regulate the ck usage in a situation of induction are not well understood. ## mechanistic study we present the metrics in 5.1, and describe our results in 5.2. ## conclusion in this paper, we demonstrated that entropy neurons play a significant role in modulating the balance between pk and ck. ablation studies revealed that perturbing these neurons leads to significant shifts in the knowledge source used by the model. specifically, the gts for entropy neurons is at the top 1% of the control distribution, this finding is consistent for different models up to 8b parameters. more broadly, identifying entropy neurons as inhibitors of context copying contributes to a clearer picture of how llms manage conflicting sources of knowledge. this lays the groundwork for future work on characterizing and interpreting the internal dynamics of llms, and more specifically helping to explain when and why models rely on pk vs ck.