1 citations · 1 across the 3 of their papers we have counts for
8 papers
Embarrassingly Simple Self-Distillation Improves Code Generation
Ruixiang Zhang, Richard He Bai, Huangjie Zheng +3
Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirma…
Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling
Yizhu Jiao, Ruixiang Zhang, Richard Bai +3
Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, often a single pass/fail bit. Te…
Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis
Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang +3
Unified decoder-only transformers have shown promise for multimodal generation, yet the mechanisms by which they synchronize modalities with heterogeneous sampling rates remain und…
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…
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…
ChipChat: Low-Latency Cascaded Conversational Agent in MLX
Tatiana Likhomanenko, Luke Carlson, Richard He Bai +8
The emergence of large language models (LLMs) has transformed spoken dialog systems, yet the optimal architecture for real-time on-device voice agents remains an open question. Whi…