arXiv:2606. 07555v1 Announce Type: cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways.
By Han-yu Wang
Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test the prediction that a distant discourse anchor can...
The paper introduces MWE‑ECL, a bilingual diagnostic framework that tests whether distant discourse anchors can override local lexical priors in multi‑word expression interpretation. It evaluates models on a 0‑128K context grid, finding that while retrieval of anchors is near perfect, the ability to change locally preferred readings varies, especially when the model’s default conflicts with the anchor. The study shows that explicit recoverability does not always translate into behavioral influence, with gaps differing across models and languages.
By Wei He, Aline Villavicencio, Rodrigo Wilkens, Zhenyun Deng
arXiv:2603. 00270v3 Announce Type: replace-cross Abstract: Large language models can process millions of tokens, yet how they handle conflicting information within context remains poorly understood.
By Sourav Chattaraj, Kanak Raj
The paper investigates why Word-in-Context (WiC) remains difficult for language models, suggesting that the lack of an explicit sense inventory contributes to the challenge. By evaluating open LLMs on both WiC and traditional Word Sense Disambiguation (WSD) tasks, the authors find that providing candidate senses—akin to WSD—consistently improves WiC performance. Human evaluation indicates that many WiC errors stem from label ambiguity or mismatched sense boundaries, with models often over‑discriminating senses and making overly fine‑grained distinctions.
By Yi Zhou, Kiamehr Rezaee, Danushka Bollegala, Mohammad Taher Pilehvar, Jose Camacho-Collados
The paper investigates how large language models handle domain-specific jargon, comparing a general-purpose Llama‑3.1 with a version fine‑tuned on medical data. Two new medical jargon benchmarks reveal that the general model actually outperforms the fine‑tuned variant, and interpretability tools show the fine‑tuned model over‑emphasizes a few components linked to jargon predictions. Reweighting these components narrows the performance gap, and some jargon‑sensitive components also aid materials‑science tasks, indicating a partially domain‑agnostic representation of specialized terminology.
By Darin Keng, Zhewei Sun