Summary of Order-independence Without Fine Tuning, by Reid Mcilroy-young et al.
Order-Independence Without Fine Tuning
by Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang, Cynthia Dwork
First submitted to arxiv on: 4 Jun 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 This paper presents Set-Based Prompting, a technique to eliminate order dependency in Large Language Models (LLMs) when generating text based on multiple-choice questions or analyzing multiple inputs. Unlike humans, LLMs are sensitive to small changes in their inputs, leading to inconsistent behavior. The authors show that their method provably eliminates order dependency and can be applied to any transformer-based LLM. This enables text generation unaffected by re-orderings, with a small impact on expected accuracy even when inputs are out of distribution. Set-Based Prompting is a ‘dropped-in’ method that can be used on fully trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can generate long and coherent texts via autoregression. However, these models are sensitive to small changes in their inputs, leading to inconsistent behavior. The authors of this paper present Set-Based Prompting, a technique that eliminates order dependency when generating text based on multiple-choice questions or analyzing multiple inputs. This means that the output will not change significantly if sub-sequences are swapped, despite being semantically identical. |
Keywords
» Artificial intelligence » Prompting » Text generation » Transformer
