Behavioral game theory offers a valuable framework for understanding LLM behavior and highlights the need for further research to develop more socially intelligent and aligned AI systems.
This is a study of Behavior of Large Language Models (LLMs) when playing repeated games. The study uses behavioral game theory to analyze LLMs' cooperation and coordination abilities, comparing their performance against each other and against human players.
* **LLMs excel in self-interested games:** They perform well in games like the Prisoner's Dilemma, demonstrating a tendency towards defection and a lack of forgiveness.
* **LLMs struggle with coordination:** They underperform in games requiring coordination, such as the Battle of the Sexes, often failing to adapt to simple strategies.
* **Prompting can influence behavior:** Providing additional information about the opponent or using a "social chain-of-thought" (SCoT) prompting strategy can improve LLM performance, particularly in coordination games.
* **Human experiments confirm findings:** Human participants interacting with LLMs showed increased coordination and cooperation when the LLMs were prompted with SCoT.
This is a study of Behavior of Large Language Models (LLMs) when playing repeated games. The study uses behavioral game theory to analyze LLMs' cooperation and coordination abilities, comparing their performance against each other and against human players.
* **LLMs excel in self-interested games:** They perform well in games like the Prisoner's Dilemma, demonstrating a tendency towards defection and a lack of forgiveness.
* **LLMs struggle with coordination:** They underperform in games requiring coordination, such as the Battle of the Sexes, often failing to adapt to simple strategies.
* **Prompting can influence behavior:** Providing additional information about the opponent or using a "social chain-of-thought" (SCoT) prompting strategy can improve LLM performance, particularly in coordination games.
* **Human experiments confirm findings:** Human participants interacting with LLMs showed increased coordination and cooperation when the LLMs were prompted with SCoT.
- TabPFN is a novel foundation model designed for small- to medium-sized tabular datasets, with up to 10,000 samples and 500 features.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
Scientists are exploring the capabilities of the DeepSeek-R1 AI model, released by a Chinese firm. This open and cost-effective model performs comparably to industry leaders in solving mathematical and scientific problems. Researchers are leveraging its accessibility to create custom models for specific disciplines, although it still struggles with some tasks.
Large language models (LLMs) are traditionally used online, but open-weights versions and smaller models are changing that, enabling researchers to run powerful AI tools locally for privacy, reproducibility, and cost-effectiveness.
The article provides examples of researchers using local models for various tasks, including:
- Summarizing scientific data and publications
- Generating training data for other models
- Transcribing and summarizing patient interviews
- Designing novel proteins
The article provides examples of researchers using local models for various tasks, including:
- Summarizing scientific data and publications
- Generating training data for other models
- Transcribing and summarizing patient interviews
- Designing novel proteins
