Junxian He, Zhenzhong Lan, Junlei Zhang
We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sjtu-lit/syncse | canonical | 2 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| cl_forward | Ran | sjtu-lit/syncse/simcse/models.py code served (permissive licence) · get_code("4e0ff9c4d9a318ff") |
| sentemb_forward | Ran | sjtu-lit/syncse/simcse/model_mp.py code served (permissive licence) · get_code("cb67cd5ac977480d") |
| cl_forward | Not yet run | sjtu-lit/syncse/simcse/model_mp.py code served (permissive licence) · get_code("395a2d0164d051ea") |
| cl_forward | Not yet run | sjtu-lit/syncse/simcse/models_HSCL.py code served (permissive licence) · get_code("54ed7cc4d58f4f97") |
| cl_forward | Not yet run | sjtu-lit/syncse/simcse/models_aug.py code served (permissive licence) · get_code("1e563fb02ef1365c") |
| cl_forward | Not yet run | sjtu-lit/syncse/simcse/models_hard.py code served (permissive licence) · get_code("c34d7c6db1fe73eb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences in an unsupervised manner. However, even in the case of 1;unlabeled data, their acquisition presents challenges in certain domains due to various reasons. To address these issues, we present SynCSE, a contrastive learning framework that trains sentence embeddings with synthesized data. Specifically, we explore utilizing large language models to synthesize the required data samples for contrastive learning, including (1) producing positive and negative annotations given unlabeled sentences (SynCSE-partial), and (2) generating sentences along with their corresponding annotations from scratch (SynCSE-scratch). Experimental results on sentence similarity and reranking tasks indicate that both SynCSE-partial and SynCSE-scratch greatly outperform unsupervised baselines, and SynCSE-partial even achieves comparable performance to the supervised models in most settings. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.15077")
get_code_for_paper("2305.15077")
have("2305.15077")
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