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20242026
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cs.CL2026

Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility

Shramay Palta, Peter Rankel, Sarah Wiegreffe +1

We investigate the degree to which human (and LLM) plausibility judgments of multiple-choice commonsense benchmark answers are subject to influence by (im)plausibility arguments fo…

cs.CL2026

Localizing Anchoring Pathways in Language Models

Hillary N. Owusu, Sarah Wiegreffe, Naomi H. Feldman

Irrelevant numbers in a prompt can shift language model judgments, producing anchoring effects in numerical reasoning. We study where this anchor-sensitive signal is carried inside…

cs.CL2026

Quantifying the Gap between Understanding and Generation within Unified Multimodal Models

Chenlong Wang, Yuhang Chen, Zhihan Hu +4

Recent advances in unified multimodal models (UMM) have demonstrated remarkable progress in both understanding and generation tasks. However, whether these two capabilities are gen…

cs.CL2025

On Linear Representations and Pretraining Data Frequency in Language Models

Jack Merullo, Noah A. Smith, Sarah Wiegreffe +1

Pretraining data has a direct impact on the behaviors and quality of language models (LMs), but we only understand the most basic principles of this relationship. While most work f…

cs.CL2025

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions

Sarah Wiegreffe, Oyvind Tafjord, Yonatan Belinkov +2

Multiple-choice question answering (MCQA) is a key competence of performant transformer language models that is tested by mainstream benchmarks. However, recent evidence shows that…

cs.CL2024

The Art of Saying No: Contextual Noncompliance in Language Models

Faeze Brahman, Sachin Kumar, Vidhisha Balachandran +11

Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of "unsafe" queries…