Summary of Uncertainty Quantification in Fine-tuned Llms Using Lora Ensembles, by Oleksandr Balabanov et al.
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
by Oleksandr Balabanov, Hampus Linander
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 addresses the limitation of fine-tuning large language models (LLMs) without understanding what they have learned or forgotten. To overcome this, the authors propose principled uncertainty quantification for fine-tuned LLMs using posterior approximations and computationally efficient low-rank adaptation ensembles. They apply their method to three multiple-choice datasets using Mistral-7b and analyze the results, drawing conclusions on perceived complexity and model efficacy across different target domains during and after fine-tuning. The authors also explore entropic uncertainty measures to identify signals from inherently difficult data domains for a given architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can better understand what large language models have learned or forgotten when we adjust them to do specific tasks. Right now, it’s hard to know if these models are making good predictions because they’re not very transparent. The authors came up with a new way to measure the uncertainty of these fine-tuned models and tested it on three different datasets. They found that this method helps us understand how well the model is doing and which types of data are most challenging for it to learn. |
Keywords
* Artificial intelligence * Fine tuning * Low rank adaptation
