4 citations · 6 across the 9 of their papers we have counts for
9 papers
CoDi: Conversational Distillation for Grounded Question Answering
Patrick Huber, Arash Einolghozati, Rylan Conway +6
Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in t…
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Xi Victoria Lin, Akshat Shrivastava, Liang Luo +5
We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text…
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding
Trang Le, Daniel Lazar, Suyoun Kim +6
Spoken Language Understanding (SLU) is a critical component of voice assistants; it consists of converting speech to semantic parses for task execution. Previous works have explore…
Small But Funny: A Feedback-Driven Approach to Humor Distillation
Sahithya Ravi, Patrick Huber, Akshat Shrivastava +4
The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative…
Augmenting text for spoken language understanding with Large Language Models
Roshan Sharma, Suyoun Kim, Daniel Lazar +7
Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from input speech. Training robust models for existing application domains represented in training d…
Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding
Suyoun Kim, Akshat Shrivastava, Duc Le +3
End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic parse from speech have become more promising recently. This approach uses a single model that…