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PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models

Neural Information Processing Systems

Models (LLMs) can sometimes surpass those annotated by experts, such as journalists, according to human evaluations. However, there is limited research on whether these generic summaries meet the individual needs of ordinary people.





Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

Neural Information Processing Systems

Project lead, main contributor, correspondence to alexandre.rame@isir.upmc.fr. Equal experimental contribution, order determined at random. Further information and resources related to this project can be found on this website.