Summary of Abdelhak at Semeval-2024 Task 9 : Decoding Brainteasers, the Efficacy Of Dedicated Models Versus Chatgpt, by Abdelhak Kelious et al.
Abdelhak at SemEval-2024 Task 9 : Decoding Brainteasers, The Efficacy of Dedicated Models Versus ChatGPT
by Abdelhak Kelious, Mounir Okirim
First submitted to arxiv on: 24 Feb 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 study presents a bespoke machine learning model designed for solving the BRAINTEASER task 9, which evaluates lateral thinking capabilities through sentence and word puzzles. The introduced model achieves impressive results, ranking first in sentence puzzle resolution with an overall score of 0.98 during the test phase. Furthermore, this research investigates the performance variation of ChatGPT by analyzing how temperature settings influence its capacity for lateral thinking and problem-solving. Notably, the study reveals a significant performance gap between the dedicated model and ChatGPT, highlighting the potential benefits of specialized approaches in enhancing creative reasoning in AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test artificial intelligence’s ability to think creatively. It creates a special puzzle that requires lateral thinking to solve. The researchers made a custom AI model specifically for this task and it does incredibly well, solving puzzles better than any other model tested. They also looked at how a popular existing AI model, ChatGPT, performs on the same task when its settings are changed. Surprisingly, the new model is much better at solving these puzzles than ChatGPT, showing that making an AI specifically for creative thinking can really help it excel. |
Keywords
» Artificial intelligence » Machine learning » Temperature
