Summary of Heuristics and Biases in Ai Decision-making: Implications For Responsible Agi, by Payam Saeedi and Mahsa Goodarzi and M Abdullah Canbaz
Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI
by Payam Saeedi, Mahsa Goodarzi, M Abdullah Canbaz
First submitted to arxiv on: 26 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 cognitive biases in three large language models: GPT-4o, Gemma 2, and Llama 3.1. It conducts 1,500 experiments across nine established cognitive biases to assess the models’ responses and consistency. The results show that GPT-4o performs well overall, while Gemma 2 excels at addressing specific biases like sunk cost fallacy and prospect theory, although its performance varies across different biases. Llama 3.1 underperforms consistently, relying on heuristics and exhibiting frequent inconsistencies. The findings highlight the challenges of achieving robust reasoning in language models and emphasize the need for further development to mitigate biases in artificial general intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how three big language models think about things that can be misleading or not true (biases). They do 1,500 tests on these models to see if they can correct themselves when they make mistakes. One model does pretty well overall, but another one is good at fixing some specific biases. The third model doesn’t do as well and often makes the same mistake twice. This shows that making smart language models is hard and we need to keep working on it to make them better. |
Keywords
» Artificial intelligence » Gpt » Llama
