Summary of Dynamic Strategy Planning For Efficient Question Answering with Large Language Models, by Tanmay Parekh et al.
Dynamic Strategy Planning for Efficient Question Answering with Large Language Models
by Tanmay Parekh, Pradyot Prakash, Alexander Radovic, Akshay Shekher, Denis Savenkov
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a novel technique called DyPlan, which induces a dynamic strategy selection process in Large Language Models (LLMs) for question answering. The approach incorporates an initial decision step that selects the most suitable strategy based on the input question and guides the LLM’s response generation accordingly. To further enrich the generated answer, the authors extend DyPlan to DyPlan-verify, adding an internal verification and correction process. Experimental results on three prominent multi-hop question answering (MHQA) datasets show that DyPlan can improve model performance by 7-13% while reducing costs by 11-32% relative to the best baseline model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DyPlan is a new way to help Large Language Models answer questions better. Instead of using just one strategy, it chooses the best approach based on the question and makes adjustments as needed. This helps improve results and reduces extra work for the AI model. The researchers tested DyPlan on three big datasets and found that it works well, making answers 7-13% better while being more efficient by 11-32%. |
Keywords
» Artificial intelligence » Question answering
