Summary of Llms As Function Approximators: Terminology, Taxonomy, and Questions For Evaluation, by David Schlangen
LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluation
by David Schlangen
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 research paper explores the evolution of Natural Language Processing models from specific tasks to generalist models. The authors argue that the increasing ambiguity around what these models can do has led to overhyping their abilities, warranting a reevaluation of their strengths and weaknesses. To address this, they propose framing generalist models as approximations of specialist functions based on natural language specifications. This approach highlights questions about approximation quality, discoverability, stability, and protectability of these functions. The paper will demonstrate how this framework unites various evaluation aspects from both practical and theoretical perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Natural Language Processing models have changed over time. It’s like they’ve become superheroes that can do lots of things! But the authors think we should be more careful about what we expect from them. Instead, they suggest thinking of these models as “copycats” that can mimic specific skills based on language descriptions. This changes how we think about their abilities and what we need to evaluate them. It’s a big deal! |
Keywords
» Artificial intelligence » Natural language processing
