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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.
arXiv:2607. 22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality.
arXiv:2602. 01747v2 Announce Type: replace-cross Abstract: Automated Essay Scoring (AES) plays a crucial role in education by providing scalable and efficient assessment tools.
arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
The paper investigates whether automated alignment researchers (AARs) can post‑train language models to reduce well‑characterized alignment failures such as deception, sycophancy, and jailbreaks while preserving general capability. Across ten failures, the strongest AAR methods significantly lower targeted failures and generalize to held‑out benchmarks, larger models, and multi‑turn audits. In contrast, a human baseline of 28 experienced researchers, given eight hours to devise one‑shot methods, underperformed the best AAR approaches, and providing human ideas to AARs did not improve results.
arXiv:2608.28945v1 Announce Type: new Abstract: Automating alignment research may accelerate progress toward aligned AI, but whether it does is hard to measure. Luckily, many alignment failures, such...