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This research dissects the internal mechanics of large language models to understand how they resolve coreference for both singular and plural entities using mechanistic interpretability. By identifying specific attention heads responsible for tracking ontological similarity and linguistic conjunctions, the study reveals that LLMs align with human preferences in pronoun selection when processing plural constructions. These findings advance our understanding of contextual reasoning capabilities within transformer architectures without relying on external quantum or hardware components.
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A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.03687v1 Announce Type: new Abstract: Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singular and plural entities for plural reference. We use a combination of mechanistic interpretability and attention pattern analysis to study the process in which LLMs predict a pronoun to refer back to previously mentioned entities. Using a range of causal intervention techniques, we find a set of attention heads that are responsible for (1) representing coreference information in the input, (2) identifying entities that form a plural reference, (3) transferring the information to the component that is responsible for selecting the antecedents and predicting the pronoun. We also find that LLMs align with humans in preference for plural pronoun. Specifically, entities in a plural construction are more likely to be referred to as a plural entity if they are ontologically similar and are linked by the conjunction "and".

Abstract:Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singular and plural entities for plural reference. We use a combination of mechanistic interpretability and attention pattern analysis to study the process in which LLMs predict a pronoun to refer back to previously mentioned entities. Using a range of causal intervention techniques, we find a set of attention heads that are responsible for (1) representing coreference information in the input, (2) identifying entities that form a plural reference, (3) transferring the information to the component that is responsible for selecting the antecedents and predicting the pronoun. We also find that LLMs align with humans in preference for plural pronoun. Specifically, entities in a plural construction are more likely to be referred to as a plural entity if they are ontologically similar and are linked by the conjunction "and".

Computation and Language (cs.CL)

arXiv:2609.03687 [cs.CL]

(or arXiv:2609.03687v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.03687 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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