AI Research
Technical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus
Uncovering the misalignment between model confidence and objective factual accuracy in LLMs.
A language model's confidence in an answer is often read as a proxy for how well it knows the corresponding fact. This manual documents an open toolkit built to test that reading directly: a small causal language model is fine-tuned on a corpus that consistently asserts one fabri...
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ArXiv CS.CL
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ArXiv CS.CL
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This research exposes the critical disconnect between a language model's internal confidence scores and its actual factual grounding. By training on fabricated data, the toolkit demonstrates how easily models can be manipulated into expressing misplaced certainty.
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