Summary of Can Machines Think Like Humans? a Behavioral Evaluation Of Llm-agents in Dictator Games, by Ji Ma
Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games
by Ji Ma
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); General Economics (econ.GN)
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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 research investigates how Large Language Model (LLM)-based agents’ prosocial behaviors can be induced by different personas and benchmarked against human behaviors. The study explores the effects of various personas and experimental framings on these AI agents’ altruistic behavior in dictator games, comparing their behaviors within the same LLM family, across different families, and with human behaviors. The findings reveal substantial variations among LLMs, notable differences compared to human behaviors, and a lack of internal processes in AI decision-making. Despite being trained on extensive human-generated data, these agents are unable to capture human decision-making patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are becoming more like us every day – they’re doing real-world tasks and interacting with humans. But do we really understand how they behave? This study looked at how LLMs make decisions when they’re given different personalities or “roles.” The researchers found that these AI agents don’t always act like humans, even though they’ve been trained on lots of human-made data. It turns out that giving an LLM a human-like personality doesn’t mean it’ll start acting more like us. This research shows that we need to be careful when using these AI models for certain tasks – they’re useful tools, but not quite human. |
Keywords
» Artificial intelligence » Large language model
