Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Mohammad Zbib, Hasan Abed Al Kader Hammoud, Sina Mukalled +5 authors
AraLingBench evaluates Arabic and bilingual LLMs' linguistic competence using a benchmark with expert-designed questions across grammar, morphology, spelling, reading comprehension, and syntax, revealing gaps between surface proficiency and true comprehension.
Liyan Xu, Mo Yu, Fandong Meng +1 authors
Research investigates latent planning dynamics in large language models through a probing method called Tele-Lens, revealing limited global planning and enabling improved uncertainty estimation and CoT bypass recognition.
Runnan Fang, Shihao Cai, Baixuan Li +13 authors
A scalable framework and two-phase fine-tuning strategy enhance function-calling capabilities of agents in diverse environments, improving performance on agentic benchmarks.
Xiaoran Liu, Ruixiao Li, Mianqiu Huang +10 authors
The survey examines the advancements and challenges in long-context Large Language Models (LLMs), exploring the architecture, infrastructure, training, and evaluation technologies needed to extend their context length and address the inherent trade-offs.
Junyi Hou, Andre Lin Huikai, Nuo Chen +2 authors
PaperDebugger is an in-editor academic writing assistant that integrates large language models, enabling direct interaction within LaTeX editors for document state management, revision, and literature search.
Zhengliang Shi, Yiqun Chen, Haitao Li +23 authors
Deep Research systems integrate LLMs with external tools to enhance problem-solving capabilities, involving query planning, information acquisition, memory management, and answer generation.
Daya Guo, Qihao Zhu, Dejian Yang +10 authors
DeepSeek-Coder, a series of open-source code models trained from scratch, outperforms both open-source and closed-source models across multiple benchmarks.
Zhangchen Xu, Fengqing Jiang, Luyao Niu +4 authors
Magpie, a self-synthesis method, generates high-quality instruction data from aligned LLMs, enabling competitive performance in alignment benchmarks compared to models fine-tuned with larger, supervised datasets.
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24 authors
Falcon-H1, a new series of large language models with a hybrid architecture combining Transformer-based attention and State Space Models, achieves state-of-the-art performance and efficiency across various tasks and sizes.
Peize Sun, Yi Jiang, Shoufa Chen +4 authors
LlamaGen applies the next-token prediction paradigm from large language models to image generation, achieving state-of-the-art performance with various model sizes and conditions.
Pat Verga, Sebastian Hofstatter, Sophia Althammer +6 authors
Evaluating Large Language Models using a Panel of smaller evaluators outperforms single large evaluators with reduced bias and cost.
Zhenxiong Yu, Zhi Yang, Zhiheng Jin +19 authors
Spider-Sense framework provides intrinsic and selective agent security through event-driven defense with intrinsic risk sensing, achieving low attack success and false positive rates with minimal latency overhead.
OpenAI, Ahmed El-Kishky, Alexander Wei +22 authors
General-purpose reinforcement learning applied to large language models outperforms domain-specific systems in complex coding and reasoning tasks, achieving top results in competitions without hand-crafted strategies.
Zixiang Chen, Yihe Deng, Huizhuo Yuan +2 authors
A novel self-play fine-tuning method (SPIN) enhances Large Language Models by generating its own training data and refining its policy, achieving performance on par with models trained using direct preference optimization and additional human data.
Lichao Sun, Yue Huang, Haoran Wang +64 authors
This study assesses the trustworthiness of large language models across various dimensions, including truthfulness, safety, fairness, robustness, privacy, and machine ethics, finding a positive correlation with utility and highlighting differences between proprietary and open-source models.
Omkar Thawakar, Dinura Dissanayake, Ketan More +12 authors
A framework for evaluating and improving step-by-step visual reasoning in large language models using a specialized benchmark and a novel multimodal model trained with curriculum learning.
Dawei Gao, Zitao Li, Yuexiang Xie +20 authors
AgentScope enhances agentic applications by providing flexible tool-based interactions, unified interfaces, and advanced infrastructure based on the ReAct paradigm, supporting efficient and safe development and deployment.
Vincent Perot, Kai Kang, Florian Luisier +7 authors
LMDX adapts large language models for document information extraction with grounding guarantees, setting a new state-of-the-art on VRDU and CORD benchmarks.
Yuqiao Tan, Minzheng Wang, Shizhu He +6 authors
The paper decomposes the policy of large language models into internal layer and modular policies, revealing distinct reasoning patterns across layers and proposing Bottom-up Policy Optimization to enhance performance on complex reasoning tasks.
Yiheng Liu, Hao He, Tianle Han +18 authors
The paper reviews techniques for cost-efficient training and deployment of large language models, covering aspects like data preprocessing, parallel training, model fine-tuning, and inference optimizations including model compression and memory scheduling.
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