208 citations · 453 across the 20 of their papers we have counts for
10 papers · 1 filter
Self-Correcting Large Language Models: Generation vs. Multiple Choice
Hossein A. Rahmani, Satyapriya Krishna, Xi Wang +2
Large language models have recently demonstrated remarkable abilities to self-correct their responses through iterative refinement, often referred to as self-consistency or self-re…
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey +4
Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augme…
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…
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Bo Peng, Daniel Goldstein, Quentin Anthony +27
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-value…
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…
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…
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…
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala +2
Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based opti…
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
Jwala Dhamala, Tony Sun, Varun Kumar +4
Recent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now emp…
FineText: Text Classification via Attention-based Language Model Fine-tuning
Yunzhe Tao, Saurabh Gupta, Satyapriya Krishna +3
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge,…