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  • [2501. 12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs . . .
    Abstract page for arXiv paper 2501 12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
  • Ragas: Automated Evaluation of Retrieval Augmented Generation
    We introduce Ragas (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines RAG systems are composed of a retrieval and an LLM based generation module, and provide LLMs with knowledge from a reference textual database, which enables them to act as a natural language layer between a user and textual databases
  • [2412. 19437] DeepSeek-V3 Technical Report - arXiv. org
    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-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2 Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for
  • [2402. 03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning . . .
    Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1 5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data DeepSeekMath 7B has achieved an impressive score of 51 7% on the competition
  • [2501. 00309] Retrieval-Augmented Generation with Graphs (GraphRAG)
    Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications As a
  • [2501. 19393] s1: Simple test-time scaling - arXiv. org
    Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance Recently, OpenAI's o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts We seek the simplest approach to achieve test-time scaling and strong reasoning performance First, we curate a small dataset s1K of 1,000
  • [2501. 12486] The Journey Matters: Average Parameter Count over Pre . . .
    Abstract page for arXiv paper 2501 12486: The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
  • [2401. 02954] DeepSeek LLM: Scaling Open-Source Language Models with . . .
    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, which casts a dark cloud over scaling LLMs We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations
  • [2109. 12948] Understanding and Overcoming the Challenges of Efficient . . .
    Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks However, their memory footprint and high latency are prohibitive for efficient deployment and inference on resource-limited devices In this work, we explore quantization for transformers We show that transformers have unique quantization challenges -- namely, high
  • Multi-Agent Collaboration Mechanisms: A Survey of LLMs
    With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to





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