3 citations · 15 across the 20 of their papers we have counts for
7 papers · 1 filter
MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark
S Sakshi, Utkarsh Tyagi, Sonal Kumar +6
The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benc…
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
Sreyan Ghosh, Sonal Kumar, Ashish Seth +6
Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a…
LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognition
Sreyan Ghosh, Sonal Kumar, Ashish Seth +4
Visual cues, like lip motion, have been shown to improve the performance of Automatic Speech Recognition (ASR) systems in noisy environments. We propose LipGER (Lip Motion aided Ge…
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +4
We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a…
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +4
Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, th…
Do Vision-Language Models Understand Compound Nouns?
Sonal Kumar, Sreyan Ghosh, S Sakshi +2
Open-vocabulary vision-language models (VLMs) like CLIP, trained using contrastive loss, have emerged as a promising new paradigm for text-to-image retrieval. However, do VLMs unde…
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar +3
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our a…