arXiv Machine Learning
Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons
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Assessing Readability with LLMs: The Role of Reasoning and Few-Shot Prompting
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Logits are All We Need to Adapt Closed Models
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Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent
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arXiv Computation and Language
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Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization
The paper investigates whether large language models can learn and reproduce annotator‑specific label‑explanation behavior, using two sentence‑pair tasks with four annotators each. It finds that individual annotator patterns are weak at the single‑annotation level but become detectable after reducing input‑content effects and aggregating across annotators. The authors propose cross‑annotator preference optimization (CAPO), which improves upon prompting and supervised fine‑tuning by better capturing annotator‑specific reasoning while maintaining stable attribution.