{"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/contragen-effective-contrastive-learning-for","title":"ContraCLM: Contrastive Learning For Causal Language Model","arxiv_id":"2210.01185","date":"2022-10-03","proceeding":null,"authors":["Nihal Jain","Dejiao Zhang","Wasi Uddin Ahmad","Zijian Wang","Feng Nan","Xiaopeng Li","Ming Tan","Ramesh Nallapati","Baishakhi Ray","Parminder Bhatia","Xiaofei Ma","Bing Xiang"],"abstract":"Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we present ContraCLM, a novel contrastive learning framework at both token-level and sequence-level. We assess ContraCLM on a variety of downstream tasks. We show that ContraCLM enhances discrimination of the representations and bridges the gap with the encoder-only models, which makes causal language models better suited for tasks beyond language generation. Specifically, we attain $44\\%$ relative improvement on the Semantic Textual Similarity tasks and $34\\%$ on Code-to-Code Search tasks. 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