# Human-Like Anaphor Resolution in Large Language Models

arXiv: https://arxiv.org/abs/2608.05630 · HTML: https://mlsanity.com/paper/2608.05630/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Probing for Bridging Inference in Transformer Language Models](https://mlsanity.com/paper/2104.09400/) (2021)
- [Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention](https://mlsanity.com/paper/2405.16042/) (2024)
- [Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://mlsanity.com/paper/2509.17796/) (2025)
- [IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark for LLMs](https://mlsanity.com/paper/2411.07466/) (2024)
- [The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories](https://mlsanity.com/paper/2501.12651/) (2025)
- [Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics](https://mlsanity.com/paper/2504.05855/) (2025)

## Abstract (arXiv)

Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science has identified multiple factors that affect the speed and success of anaphor resolution, including discourse structure, situation-model properties, and semantic factors. Here, we investigate whether these factors also affect anaphor resolution in five Large Language Models (LLMs) with open weights: GPT-2-XL, Llama-3.1-8B, Pythia-12B, Mistral-7B, and Mistral-24B. To model processing difficulty, we adopt the standard linking hypothesis that relates human reading times to model surprisal at the anaphor. As a second behavioral measure, we compare model accuracy to human accuracy on comprehension questions probing the antecedents of anaphors. The results show selective cognitive alignment: some LLMs exhibit human-like sensitivity to discourse prominence and distance-based factors in anaphor resolution, while showing weaker or absent sensitivity to semantic interference effects. These findings delimit the conditions under which LLMs approximate human anaphor resolution.