Summary of Benchmarking Gpt-4 on Algorithmic Problems: a Systematic Evaluation Of Prompting Strategies, by Flavio Petruzzellis et al.
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies
by Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Medium Difficulty summary: This paper explores the capabilities of Large Language Models (LLMs) like GPT-4, which have transformed Natural Language Processing. While LLMs excel on various downstream tasks with minimal fine-tuning, they lack systematic generalization, making it difficult to extrapolate their knowledge outside the training data. The authors benchmark GPT-4’s performance on three algorithmic tasks that allow controlling problem difficulty using two parameters. Compared to its predecessor (GPT-3.5) and a variant of Transformer-Encoder (Neural Data Router), GPT-4 achieves superior accuracy when equipped with advanced prompting techniques, solidifying the state-of-the-art LLMs as a strong baseline even in challenging tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper looks at how well Large Language Models (LLMs) like GPT-4 can solve problems. While these models are great at doing various tasks after being trained on lots of text data, they struggle to apply what they learned to new situations. The authors tested GPT-4’s skills on three math-like tasks that let them adjust the difficulty level. They compared it to older versions of the model and another approach called Neural Data Router. By using special instructions, GPT-4 performed better than the other models. This shows that even really advanced LLMs are good at solving challenging problems. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Generalization » Gpt » Natural language processing » Prompting » Transformer
