Summary of Combining Confidence Elicitation and Sample-based Methods For Uncertainty Quantification in Misinformation Mitigation, by Mauricio Rivera et al.
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation
by Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany, Kellin Pelrine
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large Language Models (LLMs) have shown promise in tackling misinformation, but existing approaches struggle with hallucinations and overconfident predictions. To address this, we propose an uncertainty quantification framework that combines direct confidence elicitation and sampled-based consistency methods to provide better calibration for NLP misinformation mitigation solutions. Our approach investigates the calibration of sample-based consistency methods, evaluates the performance and distributional shift of robust numeric verbalization prompts, and compares the performance of GPT models with different versions and numerical scales. We also introduce a hybrid framework that combines sample-based consistency and verbalized methods to yield better uncertainty estimation for LLMs. Our work aims to improve the reliability of LLMs in misinformation mitigation applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could trust computers more when they tell you what’s true or false online. This is a big problem, as fake news and misinformation are everywhere. Researchers have been trying to solve this using special AI models called Large Language Models (LLMs). But these models often make mistakes or get too confident in their answers. To fix this, we’re proposing new ways to measure how sure an LLM is about what it says. This will help us create more reliable AI models that can better fight misinformation online. |
Keywords
» Artificial intelligence » Gpt » Nlp
