Summary of Comparative Analysis Of Demonstration Selection Algorithms For Llm In-context Learning, by Dong Shu et al.
Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context Learning
by Dong Shu, Mengnan Du
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 The paper investigates effective demonstration selection algorithms for Large Language Models (LLMs) to adapt new tasks without additional training. The proposed algorithms aim to optimize the process by selecting the best input-label pairs based on a given test input, enabling LLMs to learn relationships between examples and test inputs. However, their efficiency and effectiveness remain unclear, making it challenging to apply these algorithms in real-world scenarios. This paper evaluates six proposed algorithms on five datasets from both efficiency and effectiveness perspectives, revealing significant variations in algorithm performance across different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can learn new tasks by adapting to demonstrations without additional training. To do this effectively, the right examples are needed. Researchers have developed algorithms to pick the best examples, but it’s not clear which ones work well. This paper looks at six of these algorithms and how they perform on five different datasets. The results show that some algorithms don’t always work better than randomly picking examples, and there can be trade-offs between getting the answer right and using a lot of computer power. |
