Summary of Dynamic Intelligence Assessment: Benchmarking Llms on the Road to Agi with a Focus on Model Confidence, by Norbert Tihanyi et al.
Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence
by Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky, Rebeka Toth, Bertalan Borsos, Bilel Cherif, Mohamed Amine Ferrag, Lajos Muzsai, Ridhi Jain, Ryan Marinelli, Lucas C. Cordeiro, Merouane Debbah, Vasileios Mavroeidis, Audun Josang
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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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 introduces Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines. The accompanying dataset, DIA-Bench, contains diverse challenge templates with mutable parameters presented in various formats. Four new metrics assess a model’s reliability and confidence across multiple attempts, revealing significant gaps in models’ reliability. API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. OpenAI’s o1-mini proved to have the best judgment on what tasks it should attempt to solve. The paper evaluates 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test AI models by asking them dynamic questions. This helps to make sure the models aren’t just memorizing answers or guessing. The authors create a special dataset called DIA-Bench that has many different types of challenges for the models to solve. They also come up with four new ways to measure how well the models do, and they use these metrics to test 25 state-of-the-art AI language models. The results show that even simple questions can be tricky for these models, and they often don’t know when they’re in over their heads. |
Keywords
» Artificial intelligence » Gpt
