Summary of Grounding by Trying: Llms with Reinforcement Learning-enhanced Retrieval, By Sheryl Hsu et al.
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
by Sheryl Hsu, Omar Khattab, Chelsea Finn, Archit Sharma
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 proposes LeReT, a reinforcement learning framework that improves the search query quality of large language models (LLMs). By allowing LLMs to explore different queries and learn from successful ones, LeReT can boost absolute retrieval accuracy by up to 29% and downstream generator evaluations by 17%. The framework’s simplicity and flexibility make it applicable to various off-the-shelf retrievers, enhancing overall LLM pipelines. This work highlights the importance of optimizing search queries in LLMs, particularly when dealing with complex topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models find better answers by teaching them how to ask good questions. It’s like a person searching for information on the internet – they try different search terms until they get what they need. The researchers developed a new way to do this called LeReT, which can make big improvements in finding relevant information and generating helpful responses. This could be really useful for language models that are used for things like answering questions or summarizing text. |
Keywords
* Artificial intelligence * Reinforcement learning
