1 citations · 1 across the 10 of their papers we have counts for
12 papers
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs
Jiahao Kuang, Yangfan Gao, Tao Gui +4
Parallel generation is widely regarded as an inherent advantage of diffusion large language models. Yet our fixed-quota evaluation of four representative models reveals severe degr…
ChartE: A Comprehensive Benchmark for End-to-End Chart Editing
Shuo Li, Jiajun Sun, Zhekai Wang +9
Charts are a fundamental visualization format for structured data analysis. Enabling end-to-end chart editing according to user intent is of great practical value, yet remains chal…
The Role of Entropy in Visual Grounding: Analysis and Optimization
Shuo Li, Jiajun Sun, Zhihao Zhang +10
Recent advances in fine-tuning multimodal large language models (MLLMs) using reinforcement learning have achieved remarkable progress, particularly with the introduction of variou…
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Junjie Ye, Yuming Yang, Yang Nan +7
Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). Howe…
What Makes a Good Speech Tokenizer for LLM-Centric Speech Generation? A Systematic Study
Xiaoran Fan, Zhichao Sun, Yangfan Gao +19
Speech-language models (SLMs) offer a promising path toward unifying speech and text understanding and generation. However, challenges remain in achieving effective cross-modal ali…
WorldPM: Scaling Human Preference Modeling
Binghai Wang, Runji Lin, Keming Lu +17
Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference mode…
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models
Junjie Ye, Caishuang Huang, Zhuohan Chen +12
Instruction following refers to the ability of large language models (LLMs) to generate outputs that satisfy all specified constraints. Existing research has primarily focused on c…
Effective Length Extrapolation via Dimension-Wise Positional Embeddings Manipulation
Yi Lu, Wanxu Zhao, Xin Zhou +9
Large Language Models (LLMs) often struggle to process and generate coherent context when the number of input tokens exceeds the pre-trained length. Recent advancements in long-con…
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
Shuo Li, Jiajun Sun, Guodong Zheng +10
Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs…
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts
Ming Zhang, Yuhui Wang, Yujiong Shen +16
Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large La…
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric
Yuming Yang, Yang Nan, Junjie Ye +8
Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quali…
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
Tao Ji, Bin Guo, Yuanbin Wu +6
Multi-head Latent Attention (MLA) is an innovative architecture proposed by DeepSeek, designed to ensure efficient and economical inference by significantly compressing the Key-Val…