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Overall, the results show that SAE- and probe-based techniques often outperform basic prompt-based methods for steering LLM agents, although this advantage depends on the specific prompting strategy involved.
We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at scale and expensive. ORQA complements both of these methods by connecting O*NET occupati...
This article introduces a framework for designing and running simulated experiments with LLM‐powered agents and applies the framework to the exploration–exploitation dilemma and shows that LLM‐based experiments reproduce patterns observed among human participants.
A unified framework that evaluates the capability of models to automate and augment another agent's performance, motivating benchmarks that evaluate models according to the roles they play in human-AI and multi-agent systems.
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