82 citations · 264 across the 36 of their papers we have counts for
37 papers
Mechanistic Interpretability of Atmospheric Rivers in GraphCast
Madelyn Mathai, Timothy B. Higgins, Kevin M. Grise +2
While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution reveals which input patterns mat…
A Survey of Multilingual Reasoning in Language Models
Akash Ghosh, Debayan Datta, Sriparna Saha +1
While reasoning and multilingual capabilities in language models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm - multilingual r…
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…
On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models
Sree Harsha Tanneru, Dan Ley, Chirag Agarwal +1
As Large Language Models (LLMs) are increasingly being employed in real-world applications in critical domains such as healthcare, it is important to ensure that the Chain-of-Thoug…
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju
Large Language Models (LLMs) are deployed as powerful tools for several natural language processing (NLP) applications. Recent works show that modern LLMs can generate self-explana…
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
Tessa Han, Aounon Kumar, Chirag Agarwal +1
As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due t…
Understanding the Effects of Iterative Prompting on Truthfulness
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
The development of Large Language Models (LLMs) has notably transformed numerous sectors, offering impressive text generation capabilities. Yet, the reliability and truthfulness of…
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
Large Language Models (LLMs) are increasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Recent prompting works claim to elicit…
In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
Nicholas Kroeger, Dan Ley, Satyapriya Krishna +2
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language unders…
On the Trade-offs between Adversarial Robustness and Actionable Explanations
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
As machine learning models are increasingly being employed in various high-stakes settings, it becomes important to ensure that predictions of these models are not only adversarial…
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…
Counterfactual Explanation Policies in RL
Shripad V. Deshmukh, Srivatsan R, Supriti Vijay +2
As Reinforcement Learning (RL) agents are increasingly employed in diverse decision-making problems using reward preferences, it becomes important to ensure that policies learned b…
Explaining RL Decisions with Trajectories
Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy +4
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by…
Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation
Michael Llordes, Debasis Ganguly, Sumit Bhatia +1
Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. T…
DeAR: Debiasing Vision-Language Models with Additive Residuals
Ashish Seth, Mayur Hemani, Chirag Agarwal
Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable…
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Jiali Cheng, George Dasoulas, Huan He +2
Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world ap…
Towards Estimating Transferability using Hard Subsets
Tarun Ram Menta, Surgan Jandial, Akash Patil +6
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suita…
Towards Training GNNs using Explanation Directed Message Passing
Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez +1
With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions…
Evaluating Explainability for Graph Neural Networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju +1
As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explana…
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Chirag Agarwal, Dan Ley, Satyapriya Krishna +6
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introd…
Rethinking Stability for Attribution-based Explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk +4
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are st…
A Tale Of Two Long Tails
Daniel D'souza, Zach Nussbaum, Chirag Agarwal +1
As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. Ho…
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi +2
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explan…
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
As Graph Neural Networks (GNNs) are increasingly being employed in critical real-world applications, several methods have been proposed in recent literature to explain the predicti…