OpenAI Blog
Sep 4, 2020
Learning to summarize with human feedback
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.
AI-written critiques help humans notice flaws
We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques.
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arXiv AI
Aug 28
Evaluating AI Generated Summaries for Cancer Patients
The study evaluates AI-generated summaries for cancer patients using a dual assessment framework that includes human experts and LLM-as-a-judge. Human domain experts—oncology clinicians and patient-facing care staff—assess summary quality on accuracy, clinical relevance, and readability. The research identifies limitations such as omissions and minor inaccuracies, which are then used to iteratively refine prompts, grounding, and safety guardrails.
arXiv AI
Sep 15
AppliedScientist: Automated Scientific Revision Through Iterative AI Reviewing
arXiv:2609.14738v1 Announce Type: new Abstract: Automated reviewing systems are increasingly evaluated based on the quality of the reviews they produce. Yet a review is only useful if acting on it le...
OpenAI Blog
Sep 23, 2021
Summarizing books with human feedback
Scaling human oversight of AI systems for tasks that are difficult to evaluate.
arXiv AI
Jul 7
Stop Automating Peer Review Without Rigorous Evaluation
arXiv:2605. 03202v2 Announce Type: replace Abstract: Large language models offer a tempting solution to address the peer review crisis.
arXiv AI
Jun 9
Summarization is Not Dead Yet
arXiv:2606. 08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem.
arXiv Computation and Language
Sep 1
Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment
arXiv:2608.30842v1 Announce Type: new Abstract: Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often...
arXiv AI
Jul 1
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
arXiv:2601. 14171v2 Announce Type: replace Abstract: Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details.
arXiv AI
Jun 26
Human-AI Complementarity: A Goal for Amplified Oversight
arXiv:2510. 26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values.
arXiv AI
Aug 26
The Limits of Automatic Evaluation of Creativity in Large Language Models
The paper examines whether existing automatic methods can reliably assess creativity in text produced by large language models (LLMs). By collecting human ratings on 11 creativity dimensions for both human and AI short stories, the authors compare these judgments with automated metrics and LLM-as-a-Judge evaluations. The results show a significant misalignment: automated metrics and LLM judges favor AI-generated stories and show near-zero correlation with human assessments, revealing fundamental limitations in current computational approaches to evaluating creative text.
arXiv AI
Jun 6
Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
arXiv:2606. 05436v1 Announce Type: new Abstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care.
arXiv AI
Jun 11
GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human
arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.