Summary of Learning Vs Retrieval: the Role Of In-context Examples in Regression with Large Language Models, by Aliakbar Nafar et al.
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models
by Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework for evaluating how Generative Large Language Models (LLMs) learn through in-context learning mechanisms. The authors argue that LLMs can solve real-world regression problems by retrieving internal knowledge or learning from examples provided in context. They design experiments to measure the extent to which LLMs rely on these mechanisms, showing that the process lies on a spectrum between retrieval and learning. The study employs three LLMs and multiple datasets to demonstrate the robustness of their findings. The proposed framework sheds light on how to engineer prompts for meta-learning from in-context examples and knowledge retrieval depending on the problem being addressed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how big language models learn when given new information. These models are really good at solving problems, but we don’t fully understand how they do it. The authors want to figure out what’s happening inside these models when they’re learning from new examples. They came up with a way to test and measure this process. Their results show that the models use a mix of their own knowledge and new information to solve problems. This is important because it can help us make better language models in the future. |
Keywords
» Artificial intelligence » Meta learning » Regression
