19 citations · 29 across the 9 of their papers we have counts for
9 papers
A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs
Raghavv Goel, Risheek Garrepalli, Sudhanshu Agrawal +3
Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence deno…
What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction
Sunny Panchal, Apratim Bhattacharyya, Guillaume Berger +10
Vision-language models have shown impressive progress in recent years. However, existing models are largely limited to turn-based interactions, where each turn must be stepped (i.e…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Zhan Ling, Yunhao Fang, Xuanlin Li +5
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems. Current approaches address this challenge by samp…
Painter: Teaching Auto-regressive Language Models to Draw Sketches
Reza Pourreza, Apratim Bhattacharyya, Sunny Panchal +3
Large language models (LLMs) have made tremendous progress in natural language understanding and they have also been successfully adopted in other domains such as computer vision,…
Look, Remember and Reason: Grounded reasoning in videos with language models
Apratim Bhattacharyya, Sunny Panchal, Mingu Lee +3
Multi-modal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos. However, existing methods still fall short in tasks like causal…
Deductive Verification of Chain-of-Thought Reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li +4
Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensiv…
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Mingu Lee, Jinkyu Lee, Hye Jin Jang +3
Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables…
Query-by-example on-device keyword spotting
Byeonggeun Kim, Mingu Lee, Jinkyu Lee +2
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system…