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。
Da Yin, Faeze Brahman, Abhilasha Ravichander +4 authors
Lumos, a modular language agent framework using open-source LLMs, achieves superior performance across various tasks and demonstrates enhanced generalization.
Sheng-Chieh Lin, Luyu Gao, Barlas Oguz +4 authors
Proposes factuality-aware alignment methods for pre-trained language models to enhance factual accuracy during supervised fine-tuning and reinforcement learning, reducing hallucination.
Risto Luukkonen, Ville Komulainen, Jouni Luoma +18 authors
The study addresses the challenges of creating large language models for underrepresented languages like Finnish, developing both monolingual and multilingual models, and evaluating their performance through a newly created benchmark.
Katherine Tian, Eric Mitchell, Huaxiu Yao +2 authors
Fine-tuning language models using automatically generated factuality preference rankings improves their factual accuracy without human labeling.
Yang Zhao, Zhijie Lin, Daquan Zhou +3 authors
BuboGPT is a multi-modal LLM with visual grounding that enhances understanding and interaction between vision, audio, and language.
Josef Dai, Xuehai Pan, Ruiyang Sun +5 authors
Safe RLHF, a novel algorithm for human value alignment in LLMs, improves performance and safety by decoupling human preferences and using a Lagrangian method to balance reward and cost constraints.
Wenyu Du, Tongxu Luo, Zihan Qiu +5 authors
A depthwise stacking operator called $G_{\text{stack}}$ accelerates LLM pre-training, improving performance with less computational expense and providing guidelines for effective use.
Difei Gao, Lei Ji, Luowei Zhou +4 authors
A multi-modal AI assistant, AssistGPT, with a Plan, Execute, Inspect, and Learn (PEIL) approach integrates LLMs with various tools to handle complex visual-based tasks and achieves state-of-the-art results on benchmarks and beyond.
Cheng Li, Mingyang Zhang, Qiaozhu Mei +4 authors
A multistage and multitask framework using large language models enhances personalized text generation by improving retrieval, ranking, summarization, synthesis, and generation skills.
Yecheng Jason Ma, William Liang, Guanzhi Wang +6 authors
Eureka, an LLM-powered reward design algorithm, generates human-level reward functions enhancing reinforcement learning for complex manipulation tasks and RLHF, including pen spinning with a simulated Shadow Hand.
Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz
A fine-tuned and merged family of large language models named Platypus, using LoRA modules and a curated dataset, achieves top performance on the Open LLM Leaderboard with reduced data and compute.
Dingyao Yu, Kaitao Song, Peiling Lu +5 authors
MusicAgent is a system that integrates various music-related tools and uses large language models to automate music processing tasks based on user requests.
Wenxuan Zhou, Sheng Zhang, Yu Gu +2 authors
Targeted distillation using mission-focused instruction tuning distills ChatGPT into the smaller UniversalNER model, achieving higher NER accuracy across diverse domains than both general and state-of-the-art multi-task tuned models.
Jiaao Chen, Xiaoman Pan, Dian Yu +4 authors
A novel prompting strategy called skills-in-context (SKiC) enhances compositional generalization in large language models by enabling them to solve unseen complex problems through the synergistic activation and combination of internal skills.
Lichang Chen, Shiyang Li, Jun Yan +8 authors
A data selection strategy using a strong LLM improves instruction-following models by filtering high-quality data, enhancing performance and reducing training time.
Tianyi Chen, Tianyu Ding, Badal Yadav +2 authors
LoRAShear is an efficient method for pruning and knowledge recovery in Large Language Models, reducing their size by 20% with minimal performance loss.
Liang Wang, Nan Yang, Furu Wei
A framework is proposed for training dense retrievers to select high-quality in-context examples for large language models, enhancing their performance across various tasks.
Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang +2 authors
GPT-4 demonstrates superior self-repair capabilities on APPS dataset compared to GPT-3.5, with performance boosted when feedback is provided by GPT-4 or human programmers.
Ruoxi Sun, Sercan O Arik, Hootan Nakhost +4 authors
SQL-PaLM, an LLM-based Text-to-SQL model, outperforms existing systems in few-shot learning and fine-tuning settings and demonstrates superior generalization on diverse variants of the Spider dataset.
Xiang Deng, Yu Gu, Boyuan Zheng +5 authors
Mind2Web, a dataset for generalist web agents, uses real-world websites and crowdsourced actions to improve LLM performance and generalizability.
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