ContraCLM: Contrastive Learning For Causal Language Model

• URL: http://arxiv.org/abs/2210.01185v2
• Date: Tue, 2 May 2023 22:46:46 GMT
• Title: ContraCLM: Contrastive Learning For Causal Language Model
• 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 summary: We present ContraCLM, a novel contrastive learning framework at both token-level and sequence-level. We show that ContraCLM enhances discrimination of the representations and bridges the gap with the encoder-only models.
• Score: 54.828635613501376
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. Furthermore, by improving the expressiveness of the representations, ContraCLM also boosts the source code generation capability with $9\%$ relative improvement on execution accuracy on the HumanEval benchmark.

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