Summary of Synatra: Turning Indirect Knowledge Into Direct Demonstrations For Digital Agents at Scale, by Tianyue Ou et al.
Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale
by Tianyue Ou, Frank F. Xu, Aman Madaan, Jiarui Liu, Robert Lo, Abishek Sridhar, Sudipta Sengupta, Dan Roth, Graham Neubig, Shuyan Zhou
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper presents Synatra, a method that transforms indirect knowledge into direct supervision at scale to improve the accuracy of Large Language Models (LLMs) in completing specific objectives. The authors propose three types of indirect knowledge: online tutorials, interactive simulations, and human-provided demonstrations. They demonstrate how to encode the structure of direct demonstrations using these sources and transform the indirect knowledge into direct ones. Using 100k synthetically-created demonstrations, they fine-tune a 7B CodeLlama model and show that it surpasses comparably-sized models on three web-based task benchmarks: Mind2Web, MiniWoB++, and WebArena. Additionally, they compare the effectiveness of synthetic demonstrations with human-collected ones and find that Synatra’s approach can be more effective at a lower cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models (LLMs) get better at doing tasks on computers. Right now, LLMs are not very good at this because they don’t have enough examples of how to do these tasks correctly. The authors came up with an idea called Synatra that uses information from online tutorials and other sources to help the LLM learn. They tested their approach using a big language model and found that it was much better than similar models at doing tasks on computers. This is important because it could make computers more helpful for people in the future. |
Keywords
» Artificial intelligence » Language model
