6 papers
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
Qili Zhang, Qianren Mao, Yangyifei Luo +15
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…
Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective
Boqi Chen, Xudong Liu, Yunke Ao +1
Stochastic sampling strategies are widely adopted in large language models (LLMs) to balance output coherence and diversity. These heuristics are often inherited in Multimodal LLMs…
Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
Boqi Chen, Xudong Liu, Jianing Qiu
We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned auxiliary view. We leverag…
AMUSE: Audio-Visual Benchmark and Alignment Framework for Agentic Multi-Speaker Understanding
Sanjoy Chowdhury, Karren D. Yang, Xudong Liu +6
Recent multimodal large language models (MLLMs) such as GPT-4o and Qwen3-Omni show strong perception but struggle in multi-speaker, dialogue-centric settings that demand agentic re…
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
Jiaming Yan, Jianchun Liu, Hongli Xu +4
Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under com…
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Philip Anastassiou, Jiawei Chen, Jitong Chen +43
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-T…