arXiv:2606.16407v1 Announce Type: new Abstract: Faithful and robust pronoun use is important for fair and coherent generations, yet large language models largely fail when multiple referents use different pronouns. To study the interplay of reasoning, repetition, and bias in this task, prior work relies exclusively on behavioural approaches, which may not reflect a model's internal workings. Therefore, we provide a mechanistic, model-internal perspective on pronoun fidelity, testing whether three mechanisms -- group entity binding (G), recency bias (R), and stereotypical bias (S) -- are causal
This research is emerging now as the scaling laws of large language models are reaching a point where understanding their internal mechanisms, especially regarding complex linguistic phenomena like pronoun fidelity, becomes crucial for responsible and effective deployment.
Understanding the mechanistic failures of LLMs in pronoun usage can directly inform the development of more robust, fair, and less biased AI systems, which is critical for trust and widespread adoption.
The focus is shifting from purely behavioral testing of LLMs to a deeper, mechanistic understanding of their internal reasoning processes, providing tools to diagnose and potentially mitigate failures rather than just observe them.
- · AI developers
- · NLP researchers
- · Ethical AI advocates
- · Developers relying solely on behavioral testing
- · Systems with implicit biases
Improved methods for debugging and fine-tuning LLMs become possible due to a deeper understanding of their internal workings.
More reliable and less biased AI applications emerge, particularly in areas requiring nuanced language understanding.
Increased public and institutional trust in AI systems could accelerate adoption across sensitive domains.
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