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。
Zhongyang Li, Ziyue Li, Tianyi Zhou
A novel test-time optimization method, C3PO, improves the accuracy and efficiency of Mixture-of-Experts Large Language Models by re-weighting experts based on surrogate objectives from neighboring samples.
Chunyi Sun, Junlin Han, Weijian Deng +3 authors
3D-GPT leverages large language models to drive 3D modeling tasks, enhancing scene descriptions and interfacing with 3D software through integrated agents.
Junying Chen, Chi Gui, Anningzhe Gao +4 authors
Chain-of-Diagnosis (CoD) enhances interpretability in LLM-based medical diagnostics by providing a transparent reasoning pathway and developing DiagnosisGPT, which diagnoses a wide range of diseases with high accuracy and controllable rigor.
Yiruo Cheng, Kelong Mao, Ziliang Zhao +6 authors
CORAL is a large-scale benchmark for evaluating Retrieval-Augmented Generation (RAG) systems in multi-turn conversational settings, addressing challenges like open-domain coverage, knowledge intensity, free-form responses, and topic shifts.
Xu Huang, Wenhao Zhu, Hanxu Hu +4 authors
BenchMAX evaluates multilingual large language models across advanced capabilities like instruction following, reasoning, and code generation, revealing language-specific performance gaps.
Siyu Zhou, Tianyi Zhou, Yijun Yang +4 authors
A neurosymbolic approach aligns large language models with their environment through rule learning, improving model-based agent performance in open-world tasks.
Jean Kaddour, Joshua Harris, Maximilian Mozes +3 authors
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify the remaining challenges and already fruitful application areas. In this paper, we aim to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive.
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30 authors
Benchmark Bench assesses LLMs' capability to solve practical programming tasks by invoking function calls from various libraries, revealing significant performance gaps compared to human developers.
Guanting Dong, Xiaoshuai Song, Yutao Zhu +3 authors
VIF-RAG-QA and FollowRAG Benchmark enhance Instruction-Following alignment in RAG systems through automated instruction synthesis, verification, and evaluation.
Lifan Yuan, Ganqu Cui, Hanbin Wang +12 authors
Eurus, a suite of reasoning-optimized large language models, achieves state-of-the-art performance on various benchmarks through UltraInteract, a large-scale, high-quality alignment dataset, and a novel reward modeling objective.
Shihan Dou, Yan Liu, Haoxiang Jia +13 authors
StepCoder, a reinforcement learning framework, enhances code generation by breaking tasks into subtasks and optimizing only executed code segments, using the APPS+ dataset for training.
Xin Lai, Zhuotao Tian, Yukang Chen +3 authors
A novel method called Step-DPO enhances LLMs' mathematical reasoning by optimizing individual reasoning steps, achieving significant accuracy improvements with minimal data and training.
Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8 authors
LongRecipe extends the context window of large language models efficiently with reduced resources and preserved performance, achieving a 16-fold increase from 8k to 128k context size.
Chunyuan Li, Zhe Gan, Zhengyuan Yang +4 authors
The survey covers the development and advancements in multimodal foundation models that integrate vision and language, transitioning from specialized models to versatile assistants through various training methods and unified architectures.
Zhangchen Xu, Fengqing Jiang, Luyao Niu +2 authors
The Larger Models' Paradox reveals that larger models are not always better teachers for fine-tuning smaller models, and a new metric, Compatibility-Adjusted Reward (CAR), is introduced to measure and improve the effectiveness of response generators.
Mouxiang Chen, Lefei Shen, Zhuo Li +3 authors
The VisionTS model, leveraging a visual masked autoencoder pre-trained on ImageNet, achieves state-of-the-art zero-shot and fine-tuned forecasting performance in time series forecasting without adapting specifically to the time-series domain.
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai +5 authors
Autoregressive pre-training for vision models (AIM) scales similarly to LLMs, showing improved performance with more data and parameters, and does not exhibit performance saturation.
Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta +2 authors
ReadAgent, an LLM-based prompting system, enhances effective context length up to 20x through memory episodes and gist memories, outperforming baselines on long-document reading comprehension tasks.
Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally +2 authors
LLM-as-a-judge method for evaluating LLMs shows promise but varies in performance and alignment with human judges, with smaller models like JudgeLM-7B sometimes outperforming larger ones in ranking tasks.
Haolin Chen, Yihao Feng, Zuxin Liu +8 authors
LaTent Reasoning Optimization (LaTRO) enhances LLMs' reasoning capabilities through variational optimization of latent distributions, improving zero-shot accuracy on complex reasoning tasks.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号