2.2k citations · 3.2k across the 79 of their papers we have counts for
43 papers · 1 filter
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
ConsensusBench: Benchmark of Consensus Nodes for LLM Reasoning via Outcome Reward Densifying
Shi-Qi Yan, Chao-Hong Tan, Qian Chen +3
Reinforcement learning (RL) has become one of the primary paradigms for reasoning enhancement of large language models (LLMs). In particular, Group Relative Policy Optimization (GR…
Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens
Shi-Qi Yan, Kai-Xuan Ding, Chao-Hong Tan +4
The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack a detailed and theoretical…
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Pengchao Feng, Chao-Hong Tan, Qian Chen +3
Spoken language models (SLMs) enable natural human-computer interaction, but their reasoning ability still lags behind that of text-based large language models, especially on spoke…
Spoken Function Calling: A New Perspective on Spoken Language Understanding for Large Audio Language Models
Yuezhang Peng, Yuxin Liu, Changfeng Gao +4
Spoken Language Understanding (SLU) is the core component of task-oriented dialogue systems and a pivotal link in achieving seamless human-agent interaction. While traditional SLU…
Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition
Peng Wang, Yanqiao Zhu, Zixuan Jiang +8
Recent years have witnessed remarkable progress in automatic speech recognition (ASR), driven by advances in model architectures and large-scale training data. However, two importa…
GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling
Hao-Xiang Xu, Chong Deng, Jiaqing Liu +5
Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and broad coverage of scenario. Ho…
Fun-Audio-Chat Technical Report
Tongyi Fun Team, Qian Chen, Luyao Cheng +10
Recent advancements in joint speech-text models show great potential for seamless voice interactions. However, existing models face critical challenges: temporal resolution mismatc…
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
Yunjie Ji, Xiaoyu Tian, Sitong Zhao +5
We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-…
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Xiaoyu Tian, Yunjie Ji, Haotian Wang +5
Distillation has emerged as a practical and effective approach to enhance the reasoning capabilities of open-source language models. In this work, we conduct a large-scale empirica…
DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Although large language models (LLMs) have recently achieved remarkable performance on various complex reasoning benchmarks, the academic community still lacks an in-depth understa…
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substan…
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning
Cheng Wen, Tingwei Guo, Shuaijiang Zhao +2
Recent work shows that reinforcement learning(RL) can markedly sharpen the reasoning ability of large language models (LLMs) by prompting them to "think before answering." Yet whet…
Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability
Haotian Wang, Han Zhao, Shuaiting Chen +5
Recent advancements in large language models (LLMs), such as DeepSeek-R1 and OpenAI-o1, have demonstrated the significant effectiveness of test-time scaling, achieving substantial…
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study
Yunjie Ji, Sitong Zhao, Xiaoyu Tian +5
Enhancing the reasoning capabilities of Large Language Models (LLMs) with efficiency and scalability remains a fundamental challenge in artificial intelligence research. This paper…
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning
Ruoxi Xu, Yunjie Ji, Boxi Cao +7
Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domai…
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes sub…
1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training
Han Zhao, Haotian Wang, Yiping Peng +5
The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces for general reasoning tasks, composed of high-quality and challenging reasoning problems. These problems…
Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data
Shuaijiang Zhao, Tingwei Guo, Bajian Xiang +4
The GPT-4o represents a significant milestone in enabling real-time interaction with large language models (LLMs) through speech, its remarkable low latency and high fluency not on…
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Guan Wang, Sijie Cheng, Xianyuan Zhan +3
Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT…
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model
Xianghui Sun, Yunjie Ji, Baochang Ma +1
Recently, the instruction-tuning of large language models is a crucial area of research in the field of natural language processing. Due to resource and cost limitations, several r…
Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
Yunjie Ji, Yan Gong, Yong Deng +4
Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversati…
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
Yunjie Ji, Yong Deng, Yan Gong +5
The success of ChatGPT has recently attracted numerous efforts to replicate it, with instruction-tuning strategies being a key factor in achieving remarkable results. Instruction-t…