Summary of Word Importance Explains How Prompts Affect Language Model Outputs, by Stefan Hackmann et al.
Word Importance Explains How Prompts Affect Language Model Outputs
by Stefan Hackmann, Haniyeh Mahmoudian, Mark Steadman, Michael Schmidt
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 method improves the explainability of Large Language Models (LLMs) by analyzing the statistical impact of individual words in prompts on model outputs. This approach, inspired by permutation importance, masks each word and evaluates its effect on text scores aggregated over user inputs. Unlike attention-based methods, this technique measures the importance of words based on specific metrics such as bias, reading level, and verbosity. The method also enables measuring impact when attention weights are not available. To validate the approach, the study explores the effects of adding different suffixes to system prompts and compares subsequent generations with various LLMs. Results show a strong correlation between word importance scores and expected suffix importances for multiple scoring functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have changed many things, but sometimes we don’t know how they make decisions. This is a problem because it makes us wonder if they are being fair or reliable. Researchers found a way to understand LLMs better by looking at the words in their prompts and seeing what impact each word has on what they say. They used a special method called permutation importance, which is usually used for tables of data. This new approach helps figure out how important each word is, including things like bias or how easy it is to read. It even works when we don’t have the attention weights. To test this, they added different endings to prompts and saw what happened with different LLMs. The results show that their method gives good answers. |
Keywords
» Artificial intelligence » Attention
