Summary of Red Teaming For Large Language Models at Scale: Tackling Hallucinations on Mathematics Tasks, by Aleksander Buszydlik et al.
Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks
by Aleksander Buszydlik, Karol Dobiczek, Michał Teodor Okoń, Konrad Skublicki, Philip Lippmann, Jie Yang
First submitted to arxiv on: 30 Dec 2023
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 research explores how large language models (LLMs) perform on basic math problems and algebraic tasks with different prompting techniques to evaluate their quality of outputs. The study presents a framework for generating numerical questions and puzzles procedurally, and compares results with and without various red teaming techniques applied. The findings show that structured reasoning and providing worked-out examples can slow down the deterioration of answer quality, but LLMs like gpt-3.5-turbo and gpt-4 are not well-suited for elementary calculations and reasoning tasks even when being challenged. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how good language models are at doing simple math problems and solving algebra problems. It tries different ways of asking the questions to see what makes the answers better or worse. The researchers found that giving the models examples to follow and making them think more slowly helps keep the answers correct, but even with these tricks, some models aren’t very good at this kind of problem-solving. |
Keywords
» Artificial intelligence » Gpt » Prompting
