Summary of Lacie: Listener-aware Finetuning For Confidence Calibration in Large Language Models, by Elias Stengel-eskin et al.
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models
by Elias Stengel-Eskin, Peter Hase, Mohit Bansal
First submitted to arxiv on: 31 May 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 The proposed paper introduces a novel finetuning method called LACIE (Listener-Aware Confidence Inference and Estimation) to calibrate the confidence levels of large language models (LLMs). The goal is to make LLMs more trustworthy knowledge sources by ensuring that their conveyed confidence matches their actual expertise. To achieve this, the authors develop a two-agent game framework that simulates listener feedback on an LLM’s outputs. They then finetune three pre-trained LLMs using LACIE and demonstrate improved calibration, which transfers to human listeners. The results show that trained models are more accurate in rejecting incorrect answers while maintaining acceptance for correct ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) can provide not only answers but also a level of confidence about the answer being correct. To make these models trustworthy knowledge sources, it’s essential to calibrate their confidence levels. A new method called LACIE helps achieve this by considering the listener and finetuning pre-trained LLMs. The results show that calibrated models are better at rejecting incorrect answers while still accepting correct ones. This means we can trust AI more when it provides information. |
Keywords
» Artificial intelligence » Inference
