1.6k citations · 4.2k across the 75 of their papers we have counts for
8 papers · 2 filters
CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
Jie He, Richard He Bai, Sinead Williamson +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimizati…
Closing the Gap Between Text and Speech Understanding in LLMs
Santiago Cuervo, Skyler Seto, Maureen de Seyssel +5
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counte…
Omni-Router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition
Zijin Gu, Tatiana Likhomanenko, Navdeep Jaitly
Mixture-of-experts (MoE) architectures have expanded from language modeling to automatic speech recognition (ASR). Traditional MoE methods, such as the Switch Transformer, route ex…
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Shansan Gong, Ruixiang Zhang, Huangjie Zheng +4
Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planni…
SpeakStream: Streaming Text-to-Speech with Interleaved Data
Richard He Bai, Zijin Gu, Tatiana Likhomanenko +1
The latency bottleneck of traditional text-to-speech (TTS) systems fundamentally hinders the potential of streaming large language models (LLMs) in conversational AI. These TTS sys…
SAGE: Steering Dialog Generation with Future-Aware State-Action Augmentation
Yizhe Zhang, Navdeep Jaitly
Recent advances in large language models have demonstrated impressive capabilities in task-oriented applications, yet building emotionally intelligent chatbots that can engage in n…
What Makes the Preferred Thinking Direction for LLMs in Multiple-choice Questions?
Yizhe Zhang, Richard Bai, Zijin Gu +5
Language models usually use left-to-right (L2R) autoregressive factorization. However, L2R factorization may not always be the best inductive bias. Therefore, we investigate whethe…
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models
Yifu Qiu, Varun Embar, Yizhe Zhang +3
Recent advancements in long-context language models (LCLMs) promise to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines. With their expanded context windows,…