5 papers
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna, Tessa Han, Alex Gu +3
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of…
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning
Elita Lobo, Chirag Agarwal, Himabindu Lakkaraju
Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities that find applications across diverse do…
Certifying LLM Safety against Adversarial Prompting
Aounon Kumar, Chirag Agarwal, Suraj Srinivas +3
Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt to bypass the safety guardrails of an LLM and cause it to produce ha…
More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness
Aaron J. Li, Satyapriya Krishna, Himabindu Lakkaraju
The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration…