164 citations · 853 across the 53 of their papers we have counts for
54 papers
LongReasonArena: A Long Reasoning Benchmark for Large Language Models
Jiayu Ding, Shuming Ma, Lei Cui +2
Existing long-context benchmarks for Large Language Models (LLMs) focus on evaluating comprehension of long inputs, while overlooking the evaluation of long reasoning abilities. To…
BitNet b1.58 2B4T Technical Report
Shuming Ma, Hongyu Wang, Shaohan Huang +5
We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model…
BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by activation outliers, which complicate quantization to low bit-widths. We introduce BitNet v2, a novel fram…
Bitnet.cpp: Efficient Edge Inference for Ternary LLMs
Jinheng Wang, Hansong Zhou, Ting Song +7
The advent of 1-bit large language models (LLMs), led by BitNet b1.58, has spurred interest in ternary LLMs. Despite this, research and practical applications focusing on efficient…
Next Block Prediction: Video Generation via Semi-Autoregressive Modeling
Shuhuai Ren, Shuming Ma, Xu Sun +1
Next-Token Prediction (NTP) is a de facto approach for autoregressive (AR) video generation, but it suffers from suboptimal unidirectional dependencies and slow inference speed. In…
Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
Wenkai Yang, Shuming Ma, Yankai Lin +1
Recent studies have shown that making a model spend more time thinking through longer Chain of Thoughts (CoTs) enables it to gain significant improvements in complex reasoning task…
RedStone: Curating General, Code, Math, and QA Data for Large Language Models
Yaoyao Chang, Lei Cui, Li Dong +13
Pre-training Large Language Models (LLMs) on high-quality, meticulously curated datasets is widely recognized as critical for enhancing their performance and generalization capabil…
MH-MoE: Multi-Head Mixture-of-Experts
Shaohan Huang, Xun Wu, Shuming Ma +1
Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces…
BitNet a4.8: 4-bit Activations for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their perf…
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
Jinheng Wang, Hansong Zhou, Ting Song +5
Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, present a promising approach to enhancing the efficiency of LLMs in terms of speed and energ…
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Hongyu Wang, Shuming Ma, Ruiping Wang +1
We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can…
You Only Cache Once: Decoder-Decoder Architectures for Language Models
Yutao Sun, Li Dong, Yi Zhu +6
We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. It consists of two components, i.e., a cross-decoder stacked u…
Multi-Head Mixture-of-Experts
Xun Wu, Shaohan Huang, Wenhui Wang +1
Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activat…
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Shuming Ma, Hongyu Wang, Lingxiao Ma +7
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in whic…
When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology
Wenhui Wang, Shuming Ma, Hanwen Xu +4
This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner. Specifically, we split the gigapixel image into a sequence o…
Auto-ICL: In-Context Learning without Human Supervision
Jinghan Yang, Shuming Ma, Furu Wei
With in-context learning ability, the performance of large language models can be significantly boosted when provided with appropriate context. However, existing in-context learnin…
BitNet: Scaling 1-bit Transformers for Large Language Models
Hongyu Wang, Shuming Ma, Li Dong +7
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we int…
KOSMOS-2.5: A Multimodal Literate Model
Tengchao Lv, Yupan Huang, Jingye Chen +13
The automatic reading of text-intensive images represents a significant advancement toward achieving Artificial General Intelligence (AGI). In this paper we present KOSMOS-2.5, a m…
Retentive Network: A Successor to Transformer for Large Language Models
Yutao Sun, Li Dong, Shaohan Huang +5
In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and g…
LongNet: Scaling Transformers to 1,000,000,000 Tokens
Jiayu Ding, Shuming Ma, Li Dong +5
Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressiv…
Kosmos-2: Grounding Multimodal Large Language Models to the World
Zhiliang Peng, Wenhui Wang, Li Dong +4
We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual…
On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
Liang Chen, Shuming Ma, Dongdong Zhang +2
While multilingual neural machine translation has achieved great success, it suffers from the off-target issue, where the translation is in the wrong language. This problem is more…
Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus
Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma +3
Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages. Thus, in response, the MT community has, in part, shifte…
On the Pareto Front of Multilingual Neural Machine Translation
Liang Chen, Shuming Ma, Dongdong Zhang +2
In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual…