168 citations · 871 across the 72 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
Towards Uncovering How Large Language Model Works: An Explainability Perspective
Haiyan Zhao, Fan Yang, Bo Shen +2
Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opa…
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…
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Yanchen Liu, Srishti Gautam, Jiaqi Ma +1
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful soci…
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…
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…
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
Skyler Wu, Eric Meng Shen, Charumathi Badrinath +2
Chain-of-thought (CoT) prompting has been shown to empirically improve the accuracy of large language models (LLMs) on various question answering tasks. While understanding why CoT…
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru +2
Text-to-image models take a sentence (i.e., prompt) and generate images associated with this input prompt. These models have created award wining-art, videos, and even synthetic da…
Post Hoc Explanations of Language Models Can Improve Language Models
Satyapriya Krishna, Jiaqi Ma, Dylan Slack +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationale…