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。
Akari Asai, Jacqueline He, Rulin Shao +22 authors
OpenScholar, a specialized language model, enhances scientific query synthesis by retrieving relevant passages and providing citation-backed answers, outperforming existing models in correctness and citation accuracy.
Zheng Cai, Maosong Cao, Haojiong Chen +97 authors
InternLM2 is an open-source LLM that outperforms predecessors through innovative pre-training and optimization techniques, including Supervised Fine-Tuning and Conditional Online Reinforcement Learning from Human Feedback.
Chiyu Zhang, Yifei Sun, Jun Chen +7 authors
The SPAR framework enhances content recommendations by using pretrained language models, poly-attention layers, and large language models to effectively process long user engagement histories and predict user-item interactions.
Lianghui Zhu, Xinggang Wang, Xinlong Wang
Large Language Models fine-tuned as scalable judges (JudgeLM) achieve state-of-the-art performance in evaluating open-ended benchmarks through a comprehensive dataset and benchmark, enhancing judgment efficiency and accuracy.
Chengsong Huang, Qian Liu, Bill Yuchen Lin +3 authors
LoraHub, a framework for combining LoRA modules, enables few-shot cross-task generalization and facilitates community sharing of LRA modules for large language models.
Raymond Li, Loubna Ben Allal, Yangtian Zi +64 authors
StarCoder, a 15.5B parameter LLM trained on 1 trillion tokens, outperforms other open Code LLMs across multiple languages and fine-tuned Python, with safety enhancements and publicly available under the Open Responsible AI Model license.
Zhen Zeng, William Watson, Nicole Cho +4 authors
FlowMind uses Large Language Models with a generic prompt recipe to generate automatic workflows, addressing spontaneous tasks and ensuring data integrity, and it is evaluated using a new financial dataset NCEN-QA.
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster +2 authors
Experimentation with LLMs in multi-armed bandit environments reveals that robust exploration requires specific prompts, external summarization of history, or algorithmic interventions.
Hugh Zhang, Jeff Da, Dean Lee +12 authors
Evaluation of large language models on a new math benchmark, GSM1k, reveals that many models exhibit signs of overfitting to the existing GSM8k benchmark, leading to performance drops on GSM1k.
Hongliang He, Wenlin Yao, Kaixin Ma +5 authors
WebVoyager, a Large Multimodal Model, enhances web agent performance by handling multiple modalities and real-world web interactions, achieving high success rates in practical tasks.
Houwen Peng, Kan Wu, Yixuan Wei +17 authors
A new FP8 automatic mixed-precision framework for training large language models reduces memory usage and increases speed compared to BF16 and Nvidia Transformer Engine.
Scott L. Fleming, Alejandro Lozano, William J. Haberkorn +27 authors
MedAlign, a curated dataset of natural language instructions for EHR data, evaluates the accuracy and quality of general domain LLMs in healthcare, revealing high error rates and context length dependencies.
Kai Lv, Yuqing Yang, Tengxiao Liu +3 authors
A new optimizer reduces memory usage for full parameter fine-tuning of large language models, enabling 65B model training on a single machine with limited GPUs.
Zhekai Chen, Chengqi Duan, Kaiyue Sun +4 authors
UniClawBench introduces a capability-driven benchmark for evaluating proactive agents in real-world environments using live Docker container evaluation and closed-loop assessment with multiple agent roles.
Yang Liu, Yuanshun Yao, Jean-Francois Ton +5 authors
This survey examines key dimensions of trustworthiness for large language models, including reliability, safety, fairness, misuse resistance, explainability, social norms adherence, and robustness, providing guidelines for evaluating and improving model alignment.
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.
Sang-Hoon Lee, Ha-Yeong Choi, Seung-Bin Kim +1 authors
HierSpeech++, an enhanced hierarchical variational autoencoder, achieves high-quality zero-shot speech synthesis by combining self-supervised representations and an efficient super-resolution framework.
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.
Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +19 authors
Jais and Jais-chat, Arabic-centric LLMs based on the GPT-3 architecture, outperform existing models in Arabic and are competitive in English with fewer resources.
Clifton Poth, Hannah Sterz, Indraneil Paul +7 authors
Adapters is an open-source library designed for parameter-efficient and modular transfer learning in large language models, offering a unified interface for various adapter methods.
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