{"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/caisa-at-wassa-2022-adapter-tuning-for","title":"CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction","arxiv_id":null,"date":"2022-05-01","proceeding":"WASSA (ACL) 2022 5","authors":["Allison Lahnala","Charles Welch","Lucie Flek"],"abstract":"We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022. In our experiments, we find that stacking our empathy and distress adapters on a pre-trained emotion lassification adapter performs best compared to full fine-tuning approaches and emotion feature concatenation. We make our experimental code publicly available","url_abs":"https://aclanthology.org/2022.wassa-1.31","url_pdf":"https://aclanthology.org/2022.wassa-1.31.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":"caisa-at-wassa-2022-adapter-tuning-for","repo_url":"https://github.com/caisa-lab/wassa-empathy-adapters","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}