25 citations · 64 across the 13 of their papers we have counts for
15 papers
Probing AI Safety with Source Code
Ujwal Narayan, Shreyas Chaudhari, Ashwin Kalyan +4
Large language models (LLMs) have become ubiquitous, interfacing with humans in numerous safety-critical applications. This necessitates improving capabilities, but importantly cou…
Agent Context Protocols Enhance Collective Inference
Devansh Bhardwaj, Arjun Beniwal, Shreyas Chaudhari +5
AI agents have become increasingly adept at complex tasks such as coding, reasoning, and multimodal understanding. However, building generalist systems requires moving beyond indiv…
PersonaGym: Evaluating Persona Agents and LLMs
Vinay Samuel, Henry Peng Zou, Yue Zhou +6
Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user aligned interactions across domains like education and h…
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari +5
State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful…
GEO: Generative Engine Optimization
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit +3
The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. Th…
QualEval: Qualitative Evaluation for Model Improvement
Vishvak Murahari, Ameet Deshpande, Peter Clark +4
Quantitative evaluation metrics have traditionally been pivotal in gauging the advancements of artificial intelligence systems, including large language models (LLMs). However, the…
C-STS: Conditional Semantic Textual Similarity
Ameet Deshpande, Carlos E. Jimenez, Howard Chen +6
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information…
PruMUX: Augmenting Data Multiplexing with Model Compression
Yushan Su, Vishvak Murahari, Karthik Narasimhan +1
As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications. Prior work has investigated…
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit +2
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthc…
MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez +4
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasi…
Building Scalable Video Understanding Benchmarks through Sports
Aniket Agarwal, Alex Zhang, Karthik Narasimhan +3
Existing benchmarks for evaluating long video understanding falls short on two critical aspects, either lacking in scale or quality of annotations. These limitations arise from the…
DataMUX: Data Multiplexing for Neural Networks
Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang +1
In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representati…
Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
Vishvak Murahari, Dhruv Batra, Devi Parikh +1
Prior work in visual dialog has focused on training deep neural models on VisDial in isolation. Instead, we present an approach to leverage pretraining on related vision-language d…
Improving Generative Visual Dialog by Answering Diverse Questions
Vishvak Murahari, Prithvijit Chattopadhyay, Dhruv Batra +2
Prior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach…
On Attention Models for Human Activity Recognition
Vishvak S Murahari, Thomas Ploetz
Most approaches that model time-series data in human activity recognition based on body-worn sensing (HAR) use a fixed size temporal context to represent different activities. This…