{"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/continual-training-of-language-models-for-few","title":"Continual Training of Language Models for Few-Shot Learning","arxiv_id":"2210.05549","date":"2022-10-11","proceeding":null,"authors":["Zixuan Ke","Haowei Lin","Yijia Shao","Hu Xu","Lei Shu","Bing Liu"],"abstract":"Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.","url_abs":"https://arxiv.org/abs/2210.05549v1","url_pdf":"https://arxiv.org/pdf/2210.05549v1.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":"continual-training-of-language-models-for-few","repo_url":"https://github.com/uic-liu-lab/cpt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"continual-training-of-language-models-for-few","repo_url":"https://github.com/UIC-Liu-Lab/ContinualLM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"continual-training-of-language-models-for-few","repo_url":"https://github.com/zixuanke/pycontinual","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"continual-pretraining","task_name":"Continual Pretraining"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/continual-pretraining-on-ag-news","task":"Continual Pretraining","dataset":"AG News","model":"CPT","rank_in_archive_order":1,"of":1,"metrics":{"F1 - macro":"63.77"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.05549","atlas_url":"https://app.syntology.ai/?focus=2210.05549","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}