19 citations · 37 across the 29 of their papers we have counts for
32 papers
Beyond Real: Imaginary Extension of Rotary Position Embeddings for Long-Context LLMs
Xiaoran Liu, Yuerong Song, Zhigeng Liu +6
Rotary Position Embeddings (RoPE) have become a standard for encoding sequence order in Large Language Models (LLMs) by applying rotations to query and key vectors in the complex p…
Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction
Yuerong Song, Xiaoran Liu, Ruixiao Li +5
Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive quadratic computational complexity and memory overhead d…
Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections
Bo Wang, Qinyuan Cheng, Runyu Peng +7
Post-training processes are essential phases in grounding pre-trained language models to real-world tasks, with learning from demonstrations or preference signals playing a crucial…
Pre-Trained Policy Discriminators are General Reward Models
Shihan Dou, Shichun Liu, Yuming Yang +19
We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guidi…
Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache
Xiaoran Liu, Siyang He, Qiqi Wang +9
Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rel…
LongLLaDA: Unlocking Long Context Capabilities in Diffusion LLMs
Xiaoran Liu, Yuerong Song, Zhigeng Liu +4
Large Language Diffusion Models, or diffusion LLMs, have emerged as a significant focus in NLP research, with substantial effort directed toward understanding their scalability and…
DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting
Kai Lv, Honglin Guo, Qipeng Guo +1
Large language models (LLMs) exhibit exceptional performance across a wide range of tasks; however, their token-by-token autoregressive generation process significantly hinders inf…
UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance
Yichuan Ma, Yunfan Shao, Peiji Li +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet code generation remains a major challenge. Current approaches for obtaining high-qualit…
CritiQ: Mining Data Quality Criteria from Human Preferences
Honglin Guo, Kai Lv, Qipeng Guo +8
Language model heavily depends on high-quality data for optimal performance. Existing approaches rely on manually designed heuristics, the perplexity of existing models, training c…
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…
FastMCTS: A Simple Sampling Strategy for Data Synthesis
Peiji Li, Kai Lv, Yunfan Shao +5
Synthetic high-quality multi-step reasoning data can significantly enhance the performance of large language models on various tasks. However, most existing methods rely on rejecti…
Can Language Models Learn to Skip Steps?
Tengxiao Liu, Qipeng Guo, Xiangkun Hu +4
Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intrig…
Efficient Training of Large Language Models on Distributed Infrastructures: A Survey
Jiangfei Duan, Shuo Zhang, Zerui Wang +13
Large Language Models (LLMs) like GPT and LLaMA are revolutionizing the AI industry with their sophisticated capabilities. Training these models requires vast GPU clusters and sign…
ReAttention: Training-Free Infinite Context with Finite Attention Scope
Xiaoran Liu, Ruixiao Li, Qipeng Guo +7
The long-context capability of the Large Language Models (LLM) has made significant breakthroughs, but the maximum supported context length in length extrapolation remains a critic…
Case2Code: Scalable Synthetic Data for Code Generation
Yunfan Shao, Linyang Li, Yichuan Ma +11
Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs,…
In-Memory Learning: A Declarative Learning Framework for Large Language Models
Bo Wang, Tianxiang Sun, Hang Yan +3
The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignme…
Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge
Jinlan Fu, Shenzhen Huangfu, Hang Yan +2
Large Language Models (LLMs) have recently showcased remarkable generalizability in various domains. Despite their extensive knowledge, LLMs still face challenges in efficiently ut…
LongWanjuan: Towards Systematic Measurement for Long Text Quality
Kai Lv, Xiaoran Liu, Qipeng Guo +4
The quality of training data are crucial for enhancing the long-text capabilities of foundation models. Despite existing efforts to refine data quality through heuristic rules and…
Turn Waste into Worth: Rectifying Top- Router of MoE
Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei +7
Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top- routing mechanism su…
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
Demin Song, Honglin Guo, Yunhua Zhou +8
The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural…
Data-free Weight Compress and Denoise for Large Language Models
Runyu Peng, Yunhua Zhou, Qipeng Guo +4
Large Language Models (LLMs) are reshaping the research landscape in artificial intelligence, particularly as model parameters scale up significantly, unlocking remarkable capabili…
Balanced Data Sampling for Language Model Training with Clustering
Yunfan Shao, Linyang Li, Zhaoye Fei +3
Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sam…
Identifying Semantic Induction Heads to Understand In-Context Learning
Jie Ren, Qipeng Guo, Hang Yan +4
Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness. To ga…
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li +19
The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which…