Activation steering is a popular approach for shifting LLM output toward a desired behavior, and increasingly for generating realistic synthetic data. The standard recipe estimates a steering direction by contrasting LLM activations on prompts with and without a target concept, such as truthfulness. In this paper, we s...
David Arbour, Erfan Jahanparast, Serina Chang et al.· 0 citations
Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model during debugging and find that, while it raises pass rates, it also teaches the model to edit more and to...
Yun-Kai Zhan, Miao-Sen Chai, Shang-Shang Wang et al.· 0 citations
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...
Shreyas R. Krishnan, Serina Chang, Abhishek Nagaraj· 0 citations
Anamnesis produces opinion distributions that more closely match real-world survey data than standard persona-prompting baselines, offering a transparent, reproducible, and open-source alternative to proprietary simulation services.
Song Yu, Joseph Suh, Serina Chang et al.· arXiv.org· 0 citations
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