6 citations · 6 across the 7 of their papers we have counts for
7 papers
Adobe Summit Concierge Evaluation with Human in the Loop
Yiru Chen, Sally Fang, Sai Sree Harsha +6
Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we pr…
Federated Retrieval Augmented Generation for Multi-Product Question Answering
Parshin Shojaee, Sai Sree Harsha, Dan Luo +3
Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Ass…
Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions
Xiaobin Shen, Daniel Lee, Sumit Ranjan +3
Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new us…
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration
Tanay Dixit, Daniel Lee, Sally Fang +4
Large Language Models (LLMs) are increasingly integrated into diverse applications. The rapid evolution of LLMs presents opportunities for developers to enhance applications contin…
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering
Zhengyuan Zhu, Daniel Lee, Hong Zhang +4
Recent advancements in retrieval-augmented generation (RAG) have demonstrated impressive performance in the question-answering (QA) task. However, most previous works predominantly…
GenVideo: One-shot Target-image and Shape Aware Video Editing using T2I Diffusion Models
Sai Sree Harsha, Ambareesh Revanur, Dhwanit Agarwal +1
Video editing methods based on diffusion models that rely solely on a text prompt for the edit are hindered by the limited expressive power of text prompts. Thus, incorporating a r…
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha +3
In this work, we introduce LEAD, an approach to discover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection…