arXiv:2607. 08493v1 Announce Type: new Abstract: Subjective NLP tasks often exhibit systematic annotator disagreement, requiring models that represent uncertainty rather than collapse it.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
The paper introduces the Conformal Relevance framework, which employs in-context learning example curation and ensembling to generate a score function that preserves coverage while enhancing conciseness for NLP tasks such as summarization and extractive question answering. Unlike previous methods that rely on labor-intensive, task‑specific prompt engineering, this approach requires minimal manual input. The authors validate the framework across seven NLP tasks and provide a theoretical analysis of how diversity in ensembled conformal scores can improve worst‑case sentence scores, including a saturation bound on ensemble gains.
LLM-based social simulation is a promising complement to traditional methods such as surveys and behavioral experiments. A core question is how to evaluate the fidelity of LLM-simulated human behavior...
arXiv:2606. 05376v1 Announce Type: new Abstract: Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotators.
arXiv:2606. 27536v1 Announce Type: cross Abstract: Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement.
The paper introduces NAPHA, a lightweight post‑hoc alignment method that improves large language model (LLM) predictions of human judgment distributions (HJD) by matching LLM output distributions to HJD through entropy‑based class assignment and specialized alignment models. Experiments on five datasets show that while LLMs perform near human‑level on hard‑label tasks, they struggle with soft‑label predictions, and NAPHA consistently enhances soft‑label accuracy, especially on high‑entropy instances. The study also demonstrates that better entropy class prediction can further boost NAPHA’s effectiveness.