Summary of Can We Count on Llms? the Fixed-effect Fallacy and Claims Of Gpt-4 Capabilities, by Thomas Ball et al.
Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities
by Thomas Ball, Shuo Chen, Cormac Herley
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 the evaluation of large language model (LLM) capabilities, specifically the GPT-4 model, on various deterministic tasks. The tasks involve basic calculations and input parameters drawn from well-defined populations. The study examines multiple conditions per task and conducts enough trials to detect statistically significant differences. This allows for an investigation into the sensitivity of task accuracy to query phrasing and input population. The results show that seemingly trivial modifications in the task prompt or input population can lead to large differences, exceeding sampling effects. For example, performance on a simple list-counting task varies with query phrasing, list length, list composition, and object frequency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how well language models work at doing specific tasks. The researchers tested GPT-4 on simple calculations like counting things or multiplying numbers. They gave the model different ways to ask questions and used different types of information as input. Surprisingly, the results showed that even small changes in how the question was phrased or what type of information was given could make a big difference in how well the model performed. |
Keywords
» Artificial intelligence » Gpt » Large language model » Prompt
