Summary of Enhancing Trust in Large Language Models with Uncertainty-aware Fine-tuning, by Ranganath Krishnan et al.
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning
by Ranganath Krishnan, Piyush Khanna, Omesh Tickoo
First submitted to arxiv on: 3 Dec 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 paper proposes an uncertainty-aware fine-tuning approach for large language models (LLMs) to enhance their ability to provide reliable uncertainty estimates in natural language generation tasks. This is crucial for fostering trust in generated responses and preventing the generation of incorrect information, known as LLM hallucinations. The proposed approach introduces a novel uncertainty-aware causal language modeling loss function grounded in decision theory principles. Through evaluation on multiple free-form question-answering datasets and models, the paper demonstrates that this method yields better calibrated uncertainty estimates than fine-tuning with standard causal language modeling loss. Additionally, it significantly improves the model’s ability to detect hallucinations and identify out-of-domain prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to improve large language models. These models are very good at answering questions and generating text, but sometimes they make mistakes by giving false information. To fix this problem, the team created an approach that helps the model be more honest about how sure it is of its answers. This makes it easier for people to trust the model’s responses. The new method uses a special type of math called decision theory and tests it on many different datasets and models. The results show that the new method works better than the old way of fine-tuning the models. |
Keywords
» Artificial intelligence » Fine tuning » Loss function » Question answering
