Summary of Quantifying Perturbation Impacts For Large Language Models, by Paulius Rauba et al.
Quantifying perturbation impacts for large language models
by Paulius Rauba, Qiyao Wei, Mihaela van der Schaar
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Statistics Theory (math.ST); 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 presents a novel framework called Distribution-Based Perturbation Analysis (DBPA) to quantify the impact of input perturbations on large language models (LLMs). The authors tackle the challenge of disentangling meaningful changes in model responses from inherent stochasticity by framing LLM perturbation analysis as a frequentist hypothesis testing problem. DBPA constructs null and alternative output distributions within a low-dimensional semantic similarity space using Monte Carlo sampling, enabling tractable inference without restrictive assumptions. This framework is model-agnostic, supports arbitrary input perturbations on any black-box LLM, provides interpretable p-values, and yields scalar effect sizes for chosen metrics. The authors demonstrate DBPA’s effectiveness in evaluating perturbation impacts, showcasing its versatility for perturbation analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program that can understand human language, called a large language model (LLM). Sometimes, we want to see how changing the input affects the output. But it’s hard because LLMs are very good at making random mistakes, too! To solve this problem, scientists created a new way to analyze these models, called Distribution-Based Perturbation Analysis (DBPA). It’s like taking a picture of what the model does normally and then comparing it to what happens when you change something. DBPA helps us understand how changing the input affects the output and even gives us a score to show just how much it changed things. This new method is very useful for making sure our language models are reliable and easy to understand. |
Keywords
» Artificial intelligence » Inference » Large language model
