lytang/C2D-and-D2C-MiniCheck
Usage C2D and D2C sysnthetic data are used to train the MiniCheck models from the work (GitHub Repo): π MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (link) C2D: We start with any human-written claim statement. The goal is to generate synthetic documents that require models be able to check multiple facts in the claim against multiple sentences each. D2C: We start with any human-written document to start with. The goal is to generate claims and pair themβ¦ See the full description on the dataset page: https://huggingface.co/datasets/lytang/C2D-and-D2C-MiniCheck.
| Author / organization | lytang |
|---|---|
| License (as declared) | mit |
| Downloads | 525 (as reported by Hugging Face) |
| Likes | 2 |
| Created | 14 June 2024 |
| Last updated at source | 20 June 2024 |
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525 now (+34 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- vllm-sr/Decision-1.0-Route-0.6BModel Β· used to train
Recent changes
- 17 Oct 2026 Description: Usage C2D and D2C sysnthetic data are used to train the MiniCheck models from the work (GitHub Repo): π MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (link) C2D: We start with any human-written claim statement. The goal β Usage C2D and D2C sysnthetic data are used to train the MiniCheck models from the work (GitHub Repo): π MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (link) C2D: We start with any human-written claim statement. The goal
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