Summary of Calibrating Long-form Generations From Large Language Models, by Yukun Huang et al.
Calibrating Long-form Generations from Large Language Models
by Yukun Huang, Yixin Liu, Raghuveer Thirukovalluru, Arman Cohan, Bhuwan Dhingra
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 unified calibration framework aims to improve the reliability of Large Language Models (LLMs) by treating both response correctness and confidence levels as distributions. The approach addresses the limitations of current methods, which rely on binary true/false assessments that are not suitable for long-form generation tasks. The framework is evaluated using three metrics and two confidence elicitation methods: self-consistency and self-evaluation. Experimental results show that larger models do not always guarantee better calibration, and that calibration performance depends on the metric used. The study also finds that fine-tuning, incorporating source documents, and combining self-consistency with self-evaluation can enhance calibration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a way to make Large Language Models (LLMs) more reliable by making sure they’re not too confident in their answers. This is important because LLMs are used for tasks like answering questions and summarizing text, but they can be wrong sometimes. The new method looks at both whether the answer is correct and how confident the model is in that answer. It also includes two ways to make the model more accurate: checking its own responses and evaluating its own confidence levels. Tests show that bigger models aren’t always better, and that different methods work better for different types of tasks. |
Keywords
* Artificial intelligence * Fine tuning
