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MiniCheck-DeBERTa-v3-Large

lytang

The model is based on DeBERTa-v3-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-DeBERTa-v3-Large is fine tuned from `microsoft/deberta-v3-large` (He et al., 2023) on the combination of 35K data: - 21K ANLI data (Nie et al., 2020) - 14K synthetic data generated from scratch in a structed way (more details in the paper). The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-DeBERTa-v3-Large outperform all exisiting specialized fact-checkers with a similar scale. See full results in our work.

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MiniCheck-Flan-T5-Large

lytang

📃 **MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**,(EMNLP 2024, GitHub Repo) The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-Flan-T5-Large outperform all exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4, but 400x cheaper. See full results in our work.

Text analysis

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MiniCheck-RoBERTa-Large

lytang

The model is based on RoBERTA-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-RoBERTa-Large is fine tuned from the trained RoBERTA-Large model from AlignScore (Zha et al., 2023) on 14K synthetic data generated from scratch in a structed way (more details in the paper). We also have other three MiniCheck model variants: - bespokelabs/Bespoke-Minicheck-7B (Model Size: 7B) - lytang/MiniCheck-Flan-T5-Large (Model Size: 0.8B) - lytang/MiniCheck-DeBERTa-v3-Large (Model Size: 0.4B)

Text analysis

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 3

MiniCheck-DeBERTa-v3-Large

MiniCheck-Flan-T5-Large

MiniCheck-RoBERTa-Large

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