NIPS 2024 Accepted Paper Meta Info Dataset
This dataset is collect from the NIPS 2024 OpenReview website (https://openreview.net/group?id=NeurIPS.cc/2024/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/nips2024). For researchers who are interested in doing analysis of NIPS 2024 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the NIPS 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File of Paper
{
"title": "Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans",
"url": "https://openreview.net/forum?id=pwRVGRWtGg",
"detail_url": "https://openreview.net/forum?id=pwRVGRWtGg",
"authors": "Jen-tse Huang,Man Ho LAM,Eric John Li,Shujie Ren,Wenxuan Wang,Wenxiang Jiao,Zhaopeng Tu,Michael Lyu",
"tags": "NIPS 2024,Poster",
"abstract": "Evaluating Large Language Models\u2019 (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes seven LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4, Mixtral-8x22B, and LLaMA-3.1. We find that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, i.e., EmotionBench, are publicly available at https://github.com/CUHK-ARISE/EmotionBench.",
"pdf": "https://openreview.net/pdf/4d6e71e0ca7fffae0c70fd69763ea99167e3d197.pdf"
}
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