2 citations · 3 across the 7 of their papers we have counts for
7 papers
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…
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…
MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection
Kumud Lakara, Georgia Channing, Christian Rupprecht +4
One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require doma…
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems
Pratinav Seth, Akshat Bhandari, Kumud Lakara
Deep learning models frequently suffer from various problems such as class imbalance and lack of robustness to distribution shift. It is often difficult to find data suitable for t…
Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models
Kumud Lakara, Matias Valdenegro-Toro
Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in th…
Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data
Kumud Lakara, Akshat Bhandari, Pratinav Seth +1
Most machine learning models operate under the assumption that the training, testing and deployment data is independent and identically distributed (i.i.d.). This assumption doesn'…
BERT based Transformers lead the way in Extraction of Health Information from Social Media
Sidharth R, Abhiraj Tiwari, Parthivi Choubey +5
This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of…