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LLMs Process Lists With General Filter Heads
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LLMs Process Lists With General Filter Heads

#LLMs #Filter heads #Attention mechanisms #List processing #Functional programming #Causal mediation analysis #Transformer architecture #Neural networks

📌 Key Takeaways

  • Researchers discovered LLMs use specialized 'filter heads' for list processing
  • Filter heads encode compact representations of filtering predicates similar to functional programming
  • The filtering mechanism is general and portable across different contexts and formats
  • LLMs can also use alternative strategies like direct evaluation and flag storage

📖 Full Retelling

This research significantly advances our understanding of how LLMs process structured information and implement computational operations. By revealing that these models develop human-interpretable implementations of abstract operations that generalize across contexts, the study bridges the gap between neural network behavior and traditional programming paradigms. The findings have important implications for both AI research and practical applications, suggesting that LLMs may be more transparent in their processing of structured data than previously assumed. The researchers have made their code and data publicly available, enabling further exploration and validation of these discoveries by the broader scientific community.

🏷️ Themes

Artificial Intelligence, Machine Learning, Computational Mechanisms

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Mentioned Entities

Attention (machine learning)

Attention (machine learning)

Machine learning technique

Functional programming

Programming paradigm based on applying and composing functions

Large language model

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List (abstract data type)

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}
Original Source
--> Computer Science > Artificial Intelligence arXiv:2510.26784 [Submitted on 30 Oct 2025 ( v1 ), last revised 23 Feb 2026 (this version, v2)] Title: LLMs Process Lists With General Filter Heads Authors: Arnab Sen Sharma , Giordano Rogers , Natalie Shapira , David Bau View a PDF of the paper titled LLMs Process Lists With General Filter Heads, by Arnab Sen Sharma and 3 other authors View PDF HTML Abstract: We investigate the mechanisms underlying a range of list-processing tasks in LLMs, and we find that LLMs have learned to encode a compact, causal representation of a general filtering operation that mirrors the generic "filter" function of functional programming. Using causal mediation analysis on a diverse set of list-processing tasks, we find that a small number of attention heads, which we dub filter heads, encode a compact representation of the filtering predicate in their query states at certain tokens. We demonstrate that this predicate representation is general and portable: it can be extracted and reapplied to execute the same filtering operation on different collections, presented in different formats, languages, or even in tasks. However, we also identify situations where transformer LMs can exploit a different strategy for filtering: eagerly evaluating if an item satisfies the predicate and storing this intermediate result as a flag directly in the item representations. Our results reveal that transformer LMs can develop human-interpretable implementations of abstract computational operations that generalize in ways that are surprisingly similar to strategies used in traditional functional programming patterns. Comments: Code and data at this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2510.26784 [cs.AI] (or arXiv:2510.26784v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2510.26784 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arnab Sen Sharma [ view email ] [v1] Thu, 30 Oct 2025 17:57:17 U...
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arxiv.org

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