Summary of Llm Attributor: Interactive Visual Attribution For Llm Generation, by Seongmin Lee et al.
LLM Attributor: Interactive Visual Attribution for LLM Generation
by Seongmin Lee, Zijie J. Wang, Aishwarya Chakravarthy, Alec Helbling, ShengYun Peng, Mansi Phute, Duen Horng Chau, Minsuk Kahng
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 research presents a Python library called LLM Attributor, which provides interactive visualizations to attribute large language models’ (LLMs) text generation to specific training data points. The tool aims to enhance the trustworthiness of LLMs by allowing users to inspect model behaviors and compare generated text with user-provided text. The authors describe the design of their tool and highlight usage scenarios for fine-tuning LLaMA2 models with datasets related to recent disasters and finance-related question-answer pairs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This library, called LLM Attributor, helps us understand how large language models make decisions when generating text. It’s like getting a glimpse into the model’s thought process! The tool lets users see which parts of their training data are most important for the model to generate certain texts. This can help make sure the model is producing trustworthy and accurate results. You can use it to compare your own text with what the model generates, or even fine-tune the model itself. |
Keywords
» Artificial intelligence » Fine tuning » Text generation
