Summary of Optimal Query Allocation in Extractive Qa with Llms: a Learning-to-defer Framework with Theoretical Guarantees, by Yannis Montreuil et al.
Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
by Yannis Montreuil, Shu Heng Yeo, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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 The paper addresses the inefficiencies of large language models in structured text selection, specifically extractive question answering. While these models excel in generative tasks, they struggle with query allocation, leading to high computational costs. To overcome this challenge, the authors propose a Learning-to-Defer framework that allocates queries to specialized experts, ensuring high-confidence predictions while optimizing efficiency. The framework integrates a principled allocation strategy with theoretical guarantees on optimal deferral, balancing performance and cost. Evaluations on SQuADv1, SQuADv2, and TriviaQA demonstrate enhanced answer reliability and significant computational overhead reduction, making the approach suitable for scalable and efficient extractive question answering deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers better at understanding natural language by solving a specific problem. Right now, large computer programs are really good at creating new text, but they’re not as good at picking out important parts of existing text when asked questions. This can be a big problem if you need to do this quickly or on a big scale. The researchers came up with a way to make computers work more efficiently by letting different “experts” help answer questions. They tested their idea and found that it made the answers more reliable and took less computer power, making it a useful tool for people who need to use natural language processing. |
Keywords
» Artificial intelligence » Natural language processing » Question answering
