2 citations · 3 across the 12 of their papers we have counts for
12 papers
Detecting Safety Violations Across Many Agent Traces
Adam Stein, Davis Brown, Hamed Hassani +2
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversar…
Once Upon an Input: Reasoning via Per-Instance Program Synthesis
Adam Stein, Neelay Velingker, Mayur Naik +1
Large language models (LLMs) excel at zero-shot inference but continue to struggle with complex, multi-step reasoning. Recent methods that augment LLMs with intermediate reasoning…
Towards Style Alignment in Cross-Cultural Translation
Shreya Havaldar, Adam Stein, Eric Wong +1
Successful communication depends on the speaker's intended style (i.e., what the speaker is trying to convey) aligning with the listener's interpreted style (i.e., what the listene…
Instruction Following by Principled Boosting Attention of Large Language Models
Vitoria Guardieiro, Avishree Khare, Adam Stein +1
Large language models' behavior is often shaped by instructions such as system prompts, refusal boundaries, privacy constraints, and tool-use rules that must hold at inference time…
The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models
Adam Stein, Aaditya Naik, Neelay Velingker +2
Neuro-symbolic learning was proposed to address challenges with training neural networks for complex reasoning tasks with the added benefits of interpretability, reliability, and e…
Where's the Bug? Attention Probing for Scalable Fault Localization
Adam Stein, Arthur Wayne, Aaditya Naik +2
Ensuring code correctness remains a challenging problem even as large language models (LLMs) become increasingly capable at code-related tasks. While LLM-based program repair syste…
Towards Compositionality in Concept Learning
Adam Stein, Aaditya Naik, Yinjun Wu +2
Concept-based interpretability methods offer a lens into the internals of foundation models by decomposing their embeddings into high-level concepts. These concept representations…
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Aaditya Naik, Adam Stein, Yinjun Wu +2
Finding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the…
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Anton Xue, Rajeev Alur, Eric Wong
Explanation methods for machine learning models tend not to provide any formal guarantees and may not reflect the underlying decision-making process. In this work, we analyze stabi…
TopEx: Topic-based Explanations for Model Comparison
Shreya Havaldar, Adam Stein, Eric Wong +1
Meaningfully comparing language models is challenging with current explanation methods. Current explanations are overwhelming for humans due to large vocabularies or incomparable a…
Rectifying Group Irregularities in Explanations for Distribution Shift
Adam Stein, Yinjun Wu, Eric Wong +1
It is well-known that real-world changes constituting distribution shift adversely affect model performance. How to characterize those changes in an interpretable manner is poorly…
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Aaditya Naik, Yinjun Wu, Mayur Naik +1
Machine learning models can make critical errors that are easily hidden within vast amounts of data. Such errors often run counter to rules based on human intuition. However, rules…