Update README.md (#6)
Browse files- Update README.md (d8739a50cee6567c3f10a15b88de8c20cebcdab0)
Co-authored-by: Yufang Hou <[email protected]>
README.md
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The dataset has been used in the paper to assess LLMs performance when augmented with retrieved passages containing real-world knowledge conflicts.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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<!-- This section describes the people or systems who created the annotations. -->
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Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi
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#### Personal and Sensitive Information
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The dataset has been used in the paper to assess LLMs performance when augmented with retrieved passages containing real-world knowledge conflicts.
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The following figure illustrates the evaluation process:
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<p align="center">
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<img src="./figs/Evaluation.png" width=70%/>
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</p>
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And the following table shows the performance of five LLMs (Mistral-7b-inst, Mixtral-8x7b-inst, Llama-2-70b-chat, Llama-3-70b-inst, and GPT-4) on the Wikipedia Contradict Benchmark based on rigorous human evaluations on a subset of answers for 55 instances, which corresponds to 1,375 LLM responses in total.
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<p align="center">
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<img src="./figs/table2.png" width=70%/>
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</p>
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Notes: “C”, “PC” and “IC” stand for “Correct”, “Partially correct”, “Incorrect”, respectively. “all”, “exp”, and “imp” represent for instance
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types: all instances, instances with explicit conflicts, and instances with implicit conflicts. The
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numbers represent the ratio of responses from each LLM that were assessed as “Correct, “Partially
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correct, or “Incorrect for each instance type under a prompt template. The bold numbers highlight
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the best models that correctly answer questions for each type and prompt template.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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<!-- This section describes the people or systems who created the annotations. -->
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Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi
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#### Personal and Sensitive Information
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