4 papers
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…
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
Sreyan Ghosh, Ashish Seth, Sonal Kumar +7
A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representatio…
A Closer Look at the Limitations of Instruction Tuning
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +5
Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trai…
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…