{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/llm-gem-large-language-model-guided","title":"LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper Article","arxiv_id":null,"date":"2024-03-19","proceeding":"European Chapter of the Association for Computational Linguistics (EACL) 2024 3","authors":["Md Rakibul Hasan","Md Zakir Hossain","Tom Gedeon","Shafin Rahman"],"abstract":"Empathy – encompassing the understanding and supporting others’ emotions and perspectives – strengthens various social interactions, including written communication in healthcare, education and journalism. Detecting empathy using AI models by relying on self-assessed ground truth through crowdsourcing is challenging due to the inherent noise in such annotations. To this end, we propose a novel system, named Large Language Model-Guided Empathy _(LLM-GEm)_ prediction system. It rectifies annotation errors based on our defined annotation selection threshold and makes the annotations reliable for conventional empathy prediction models, e.g., BERT-based pre-trained language models (PLMs). Previously, demographic information was often integrated numerically into empathy detection models. In contrast, our _LLM-GEm_ leverages GPT-3.5 LLM to convert numerical data into semantically meaningful textual sequences, enabling seamless integration into PLMs. We experiment with three _NewsEmpathy_ datasets involving people’s empathy levels towards newspaper articles and achieve state-of-the-art test performance using a RoBERTa-based PLM. Code and evaluations are publicly available at https://github.com/hasan-rakibul/LLM-GEm.","url_abs":"https://aclanthology.org/2024.findings-eacl.147/","url_pdf":"https://aclanthology.org/2024.findings-eacl.147v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"llm-gem-large-language-model-guided","repo_url":"https://github.com/hasan-rakibul/LLM-GEm","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}