Summary of Interactive Prompt Debugging with Sequence Salience, by Ian Tenney et al.
Interactive Prompt Debugging with Sequence Salience
by Ian Tenney, Ryan Mullins, Bin Du, Shree Pandya, Minsuk Kahng, Lucas Dixon
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sequence Salience is a novel visual tool for debugging complex large language model (LLM) prompts. Building on existing salience methods, Sequence Salience extends these approaches to tackle long texts and provides controllable aggregation of token-level salience to the word, sentence, or paragraph level. This system enables rapid iteration, allowing practitioners to refine prompts based on salience results and re-run salience on new output. Case studies demonstrate how Sequence Salience can aid practitioners in working with complex prompting strategies like few-shot learning, chain-of-thought, and constitutional principles. The tool is built upon the Learning Interpretability Tool (LIT), an open-source platform for ML model visualizations, and code, notebooks, and tutorials are available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to fix a puzzle with unclear instructions! That’s what happens when you’re working with large language models (LLMs) and need to understand what makes them make certain decisions. This new tool, called Sequence Salience, helps you do just that by highlighting important parts of the text (like sentences or paragraphs) that can guide your work. With this tool, you can quickly try out different ideas, see how they affect the model’s behavior, and refine your approach until you get the results you want. |
Keywords
» Artificial intelligence » Few shot » Large language model » Prompting » Token
