Summary of Trove: Inducing Verifiable and Efficient Toolboxes For Solving Programmatic Tasks, by Zhiruo Wang et al.
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks
by Zhiruo Wang, Daniel Fried, Graham Neubig
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach to inducing high-level functions in language models (LMs) is proposed in this paper. The authors aim to enable LM-based solutions that are more efficient, accurate, and reusable without human labor. They introduce TROVE, a training-free method for generating a verifiable and compact toolbox of functions by iteratively expanding and pruning the function set. Experimental results on 11 datasets from various domains demonstrate the effectiveness of TROVE in producing simpler and more accurate solutions compared to baselines using CODELLAMA and GPT, while utilizing significantly smaller toolboxes (79-98% reduction). Moreover, TROVE enables faster and more accurate human verification by 31% and 13%, respectively. The authors also explore the diversity of functions generated for different tasks and datasets, providing valuable insights into their characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help computers write better code without needing human help. They used language models (LMs) to create reusable blocks of code that can be easily combined to solve problems. This approach is called TROVE, and it works by starting with a small set of basic functions and gradually adding more complex ones until the desired solution is reached. The results show that TROVE produces simpler and more accurate solutions than other methods, while using much less computer memory (79-98% reduction). This could make it easier for humans to review and verify the code, saving time and effort. |
Keywords
» Artificial intelligence » Gpt » Pruning
