11 papers
Learning from 53.6K Real-World Developer Edits of AI-Generated Code
Jenny T. Liang, Mihika Bairathi, Wayne Chi +3
Imperfections in AI-generated code require that software developers modify the generated code manually, or by re-prompting an AI programming assistant. Manual code edits provide mo…
Harnessing the Latent Space: From Steering Vectors to Model Calibrators for Control and Trust
Nishant Subramani
Language models have changed from unreliable text generators to highly-capable large models with trillions of parameters. Capability increases come hand-in-hand with increases in s…
The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust
Nishant Subramani, Palash Goyal, Yiwen Song +4
As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential. Calibration is a good proxy for trust: well-calibrated c…
On the Persistent Effects of Lexicality in Large Language Models
Hammad Rizwan, Muhammad Umair Haider, Nishant Subramani +3
Representations extracted from large language models (LLMs) play an important role in many downstream applications. However, the structure of these representations is often influen…
How Much Do Circuits Tell Us? Measuring the Consistency and Specificity of Language Model Circuits
Michael Li, Nishant Subramani
The circuits framework in mechanistic interpretability aims to identify causally important sparse subgraphs of model components, typically evaluated by measuring necessity and suff…
Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models
Michael Li, Nishant Subramani
Large transformer-based language models dominate modern NLP, yet our understanding of how they encode linguistic information relies primarily on studies of early models like BERT a…