Summary of Limits Of Large Language Models in Debating Humans, by James Flamino et al.
Limits of Large Language Models in Debating Humans
by James Flamino, Mohammed Shahid Modi, Boleslaw K. Szymanski, Brendan Cross, Colton Mikolajczyk
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Applications (stat.AP)
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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: Large Language Models (LLMs) have been shown to effectively communicate with humans in various applications. This study explores the potential of LLMs as artificial partners in sociological experiments involving conversations. Researchers conducted a preregistered experiment, running multiple debate-based opinion consensus games featuring six humans, six agents, or a combination of both. The results showed that agents can focus on specific topics and improve overall productivity, outperforming human participants. However, human evaluators perceived agents as less convincing and confident compared to other humans. Notably, the behavioral patterns of agents and humans deviated significantly from each other. While LLMs demonstrated decent debating skills, their behavior exhibited distinct differences from human-generated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine having a conversation with an artificial partner that can debate and communicate just like you do with another person. This study tests the limits of these language models in sociological experiments. Researchers created several “games” where humans and/or agents had to come to an agreement on certain topics. The results showed that the computer-based agents were actually quite good at focusing on specific topics and making their points effectively. However, when humans judged the debate, they thought the agents weren’t as convincing or confident as other humans. The study found that there are some big differences in how humans and computers behave during these debates. |
