activity
20232026
most citedDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

978 citations · 1.4k across the 14 of their papers we have counts for

collaborators

14 papers

cs.CL2026

Adam's Law: Textual Frequency Law on Large Language Models

Hongyuan Adam Lu, Z. L., Victor Wei +5

While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…

cs.CL2026

Gender Bias in MT for a Genderless Language: New Benchmarks for Basque

Amaia Murillo, Olatz-Perez-de-Viñaspre, Naiara Perez

Large language models (LLMs) and machine translation (MT) systems are increasingly used in our daily lives, but their outputs can reproduce gender bias present in the training data…

cs.CL2026

MMFormalizer: Multimodal Autoformalization in the Wild

Jing Xiong, Qi Han, Yunta Hsieh +11

Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal…

cs.CL2025★ 2 cited

DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition

Z. Z. Ren, Zhihong Shao, Junxiao Song +15

We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem p…

cs.CL2025★ 978 cited

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek-AI, Daya Guo, Dejian Yang +195

General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-tho…

cs.CL2024★ 268 cited

DeepSeek-V3 Technical Report

DeepSeek-AI, Aixin Liu, Bei Feng +195

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effec…

cs.CL2024★ 3 cited

DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

Huajian Xin, Z. Z. Ren, Junxiao Song +14

We introduce DeepSeek-Prover-V1.5, an open-source language model designed for theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both training and inference…

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.AI2024

Proving Theorems Recursively

Haiming Wang, Huajian Xin, Zhengying Liu +8

Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually base…

cs.AI2024★ 6 cited

DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Huajian Xin, Daya Guo, Zhihong Shao +6

Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathem…

cs.AI2024

MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data

Yinya Huang, Xiaohan Lin, Zhengying Liu +6

Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict…

cs.AI2023★ 9 cited

LEGO-Prover: Neural Theorem Proving with Growing Libraries

Haiming Wang, Huajian Xin, Chuanyang Zheng +11

Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods…

cs.CL2023★ 2 cited

Lyra: Orchestrating Dual Correction in Automated Theorem Proving

Chuanyang Zheng, Haiming Wang, Enze Xie +6

Large Language Models (LLMs) present an intriguing avenue for exploration in the field of formal theorem proving. Nevertheless, their full potential, particularly concerning the mi…

cs.AI2023★ 10 cited

FIMO: A Challenge Formal Dataset for Automated Theorem Proving

Chengwu Liu, Jianhao Shen, Huajian Xin +9

We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems. Designed t…