Epistemic Integrity in Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908398209990656 |
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| author | Ghafouri, Bijean Mohammadzadeh, Shahrad Zhou, James Nair, Pratheeksha Tian, Jacob-Junqi Tsujimura, Hikaru Goel, Mayank Krishna, Sukanya Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin |
| author_facet | Ghafouri, Bijean Mohammadzadeh, Shahrad Zhou, James Nair, Pratheeksha Tian, Jacob-Junqi Tsujimura, Hikaru Goel, Mayank Krishna, Sukanya Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin |
| contents | Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models (LLMs) which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty LLMs hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing this miscalibration, offering a path towards correcting it and more trustworthy AI across domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Epistemic Integrity in Large Language Models Ghafouri, Bijean Mohammadzadeh, Shahrad Zhou, James Nair, Pratheeksha Tian, Jacob-Junqi Tsujimura, Hikaru Goel, Mayank Krishna, Sukanya Rabbany, Reihaneh Godbout, Jean-François Pelrine, Kellin Computation and Language Artificial Intelligence Human-Computer Interaction Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models (LLMs) which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty LLMs hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing this miscalibration, offering a path towards correcting it and more trustworthy AI across domains. |
| title | Epistemic Integrity in Large Language Models |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.06528 |