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。
Shuming Ma, Hongyu Wang, Lingxiao Ma +7 authors
A 1-bit LLM variant, BitNet b1.58, achieves comparable performance to full-precision models with reduced computational costs and introduces new scaling laws and hardware design opportunities.
Dong Guo, Faming Wu, Feida Zhu +194 authors
Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.
Zechun Liu, Changsheng Zhao, Forrest Iandola +9 authors
MobileLLM leverages deep, thin architectures and advanced techniques to achieve high accuracy in sub-billion parameter models suitable for mobile devices.
Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi +3 authors
FailSafeQA evaluates the robustness and context-awareness of large language models in financial applications through domain expertise, query completeness, linguistic accuracy, and document relevance challenges.
Kuan Li, Zhongwang Zhang, Huifeng Yin +16 authors
WebSailor, a post-training methodology, enhances open-source LLMs with sophisticated reasoning to match proprietary systems in complex information-seeking tasks.
Minju Seo, Jinheon Baek, Seongyun Lee +1 authors
PaperCoder is a multi-agent LLM framework that converts machine learning papers into functional code repositories through planning, analysis, and generation stages.
Khalil Hennara, Sara Chrouf, Mohamed Motaism Hamed +3 authors
A method for integrating a new language into a large language model enhances performance in the target language while preserving existing knowledge.
Junying Chen, Zhenyang Cai, Ke Ji +5 authors
The proposed approach enhances medical reasoning in large language models using verifiable problems, guided search, and reinforcement learning to achieve superior performance in medical problem-solving.
Minjun Kim, Hyeonseok Lim, Hangyeol Yoo +10 authors
A large-scale investigation into constructing a fully open bilingual LLM for Korean using synthetic data demonstrates that such data can sustain pretraining and achieve performance comparable to multilingual baselines.
Ningning Wang, Xavier Hu, Pai Liu +11 authors
A study on the efficiency-effectiveness trade-off in LLM-driven agent systems identifies optimal agent framework design to reduce costs while maintaining performance.
Xixi Wu, Kuan Li, Yida Zhao +11 authors
ReSum, a novel paradigm with periodic context summarization, enhances web agents' performance on knowledge-intensive tasks by overcoming context window limitations, achieving significant improvements over ReAct.
Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5 authors
LLMVoX is a lightweight, LLM-agnostic TTS system that decouples speech synthesis from model processing, ensuring high-quality speech with low latency and supporting seamless multimodal dialogue.
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.
Dahyun Kim, Chanjun Park, Sanghoon Kim +15 authors
A novel technique, depth up-scaling (DUS), efficiently enhances large language models (LLMs) without complex changes, building SOLAR 10.7B that outperforms existing open-source models in various NLP tasks, including instruction-following.
Pratyush Maini, Vineeth Dorna, Parth Doshi +26 authors
BeyondWeb, a synthetic data generation framework, outperforms existing datasets and accelerates training for large language models by optimizing multiple factors.
Qingkai Fang, Shoutao Guo, Yan Zhou +3 authors
LLaMA-Omni integrates a speech encoder, adaptor, LLM, and decoder for low-latency and high-quality speech interaction with LLMs, outperforming previous models in both content and style.
Takuya Akiba, Makoto Shing, Yujin Tang +2 authors
An evolutionary algorithm automates model merging, discovering effective combinations of open-source models without additional training data or compute, achieving state-of-the-art performance on benchmarks and culturally-aware tasks.
Seungone Kim, Jamin Shin, Yejin Cho +8 authors
Prometheus, an open-source LLM, matches GPT-4's evaluation capabilities using customized score rubrics and outperforms ChatGPT across various benchmarks.
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30 authors
The initiative builds a human-curated instruction-following dataset spanning 65 languages and creates the largest multilingual collection of instruction-following instances through templating and translating existing datasets across 114 languages, contributing datasets and platforms for participatory research.
Guosheng Dong, Da Pan, Yiding Sun +17 authors
A data processing pipeline and open-sourced details for training a large language model achieve competitive performance with commercial models.
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