activity
20172024
most citedA Survey on In-context Learning

257 citations · 621 across the 31 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CL2024★ 107 cited

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

DeepSeek-AI, Aixin Liu, Bei Feng +154

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which…

cs.LG2024

Exploring Activation Patterns of Parameters in Language Models

Yudong Wang, Damai Dai, Zhifang Sui

Most work treats large language models as black boxes without in-depth understanding of their internal working mechanism. In order to explain the internal representations of LLMs,…

cs.CL2024

Large Language Models Struggle with Unreasonability in Math Problems

Jingyuan Ma, Damai Dai, Zihang Yuan +6

Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonab…

cs.CL2024★ 1 cited

PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization

Xiangdi Meng, Damai Dai, Weiyao Luo +7

Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Rece…

cs.CL2024★ 18 cited

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Damai Dai, Chengqi Deng, Chenggang Zhao +14

In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional M…

cs.CL2024★ 95 cited

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

DeepSeek-AI, :, Xiao Bi +85

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions,…

cs.CL2024★ 1 cited

Language Models Encode the Value of Numbers Linearly

Fangwei Zhu, Damai Dai, Zhifang Sui

Large language models (LLMs) have exhibited impressive competence in various tasks, but their internal mechanisms on mathematical problems are still under-explored. In this paper,…