1 citations · 1 across the 6 of their papers we have counts for
7 papers
No More Free Lunch: Corpus Task Complexity Matters as Corpora Grow
Prasann Singhal, Amanda Bertsch, Jacob Steinhardt +1
Given a large corpus, the questions one might ask can vary -- from "When was the first human heart transplant?" to "What are all the contradictory claims in this literature?" -- bu…
ADAG: Automatically Describing Attribution Graphs
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1
In language model interpretability research, \textbf{circuit tracing} aims to identify which internal features causally contributed to a particular output and how they affected eac…
Language Model Circuits Are Sparse in the Neuron Basis
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1
The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986). Language model interpretability research has…
Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants
Vincent Huang, Dami Choi, Daniel D. Johnson +2
Interpreting the internal activations of neural networks can produce more faithful explanations of their behavior, but is difficult due to the complex structure of activation space…
Training Language Models to Explain Their Own Computations
Belinda Z. Li, Zifan Carl Guo, Vincent Huang +2
Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs'…
Establishing Best Practices for Building Rigorous Agentic Benchmarks
Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun +22
Benchmarks are essential for quantitatively tracking progress in AI. As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to e…
Eliciting Language Model Behaviors with Investigator Agents
Xiang Lisa Li, Neil Chowdhury, Daniel D. Johnson +4
Language models exhibit complex, diverse behaviors when prompted with free-form text, making it difficult to characterize the space of possible outputs. We study the problem of beh…