Summary of Epistemic Integrity in Large Language Models, by Bijean Ghafouri et al.
Epistemic Integrity in Large Language Models
by Bijean Ghafouri, Shahrad Mohammadzadeh, James Zhou, Pratheeksha Nair, Jacob-Junqi Tian, Mayank Goel, Reihaneh Rabbany, Jean-François Godbout, Kellin Pelrine
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the significant issue of epistemic miscalibration in Large Language Models (LLMs), where models confidently present information that may be false or misleading. The authors introduce a novel method for measuring LLMs’ linguistic assertiveness, which outperforms previous benchmarks by over 50%. This method is validated across multiple datasets and reveals a stark misalignment between models’ confidence levels and their actual accuracy. Human evaluations confirm the severity of this miscalibration, highlighting the urgent risk of overstated certainty in AI-generated information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper investigates why large language models often confidently present false or misleading information. The authors create a new way to measure how confident these models are when presenting information and test it on multiple datasets. They find that there’s a big gap between the confidence of these models and their actual accuracy. This is an important problem because it can lead to users being misled by AI-generated information. |
