| Challenge: | Natural Language Inference (NLI) is a foundational understanding task in language understanding. |
| Approach: | They propose a framework to construct counterfactual reasoning data and fine-tune LLMs to reduce attestation bias. |
| Outcome: | The proposed framework reduces hallucinations from attestation bias on original and bias-neutralized datasets while keeping hypotheses unchanged. |
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| Challenge: | Large language models inherit societal biases against protected groups and can be subject to functionally resembling cognitive bias. |
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| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
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| Challenge: | Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI) |
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Explicit Inductive Inference using Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) suffer a signifi- cant performance drop when entailment labels disagree with the attestation label of hypothesis H. |
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