activity
20202026
most citedBuilding Proactive Voice Assistants: When and How (not) to Interact

5 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Fence: Specialized SLM Guardrails for LLM Applications

Kumud Lakara, Ruibo Shi, Fran Silavong

Real-world applications that use closed-source large language models (LLMs) need advanced safety measures that go beyond the basic content filters. Content moderation filters such…

cs.CL2025

TrueBrief: Faithful Summarization through Small Language Models

Kumud Lakara, Ruibo Shi, Fran Silavong

Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text; however, their propensity for producing hallucinations poses a significant chall…

cs.LG2023★ 1 cited

Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training

Xiaoying Zhi, Varun Babbar, Rundong Liu +4

The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutio…

cs.SE2022

CV4Code: Sourcecode Understanding via Visual Code Representations

Ruibo Shi, Lili Tao, Rohan Saphal +2

We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from t…

cs.HC2020★ 5 cited

Building Proactive Voice Assistants: When and How (not) to Interact

O. Miksik, I. Munasinghe, J. Asensio-Cubero +17

Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other…