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。
Xuefei Ning, Zinan Lin, Zixuan Zhou +2 authors
"Skeleton-of-Thought" (SoT) method reduces generation latency and potentially improves answer quality by guiding LLMs to generate a skeleton first, followed by parallel content completion.
Stephen Casper, Xander Davies, Claudia Shi +29 authors
RLHF is analyzed for its limitations and flaws, with suggestions for improvement, complementation, and oversight to enhance safer AI development.
Xing Han Lù, Zdeněk Kasner, Siva Reddy
A benchmark and retrieval-inspired model for efficient conversational web navigation by digital agents across diverse real-world websites is introduced, highlighting challenges in generalization to unseen sites.
Jinze Bai, Shuai Bai, Yunfei Chu +45 authors
Qwen, a series of large language models, including chat and specialized coding and mathematics variants, exhibit superior performance across various tasks and outperform open-source models.
SeungHeon Doh, Keunwoo Choi, Jongpil Lee +1 authors
A large-scale pseudo music caption dataset generated using LLMs improves music captioning performance over supervised models in zero-shot and transfer-learning settings.
Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran +3 authors
A new math instruction tuning dataset, OpenMathInstruct-1, leverages the Mixtral model to achieve competitive performance on math reasoning benchmarks using open-source LLMs.
Dongzhi Jiang, Renrui Zhang, Ziyu Guo +10 authors
MMSearch-Engine enables multimodal search capabilities in Large Multimodal Models (LMMs) using a comprehensive benchmark and demonstrates improved performance over commercial products.
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia +6 authors
CALM, a composition method for large language models, enhances capabilities by integrating cross-attention with specialized models, demonstrating improvements on translation and coding tasks.
Sihang Li, Jin Huang, Jiaxi Zhuang +7 authors
A hybrid approach combining continual pre-training and supervised fine-tuning creates specialized LLMs for scientific literature, addressing challenges in constructing corpora and generating instructions.
Izzeddin Gur, Hiroki Furuta, Austin Huang +4 authors
WebAgent uses Flan-U-PaLM and HTML-T5 to improve task success in real-world web navigation through decomposed instructions, HTML summarization, and Python code generation.
Jie Huang, Xinyun Chen, Swaroop Mishra +4 authors
This paper evaluates the intrinsic self-correction capabilities of Large Language Models and identifies limitations in their ability to improve responses without external feedback.
Yung-Sung Chuang, Yujia Xie, Hongyin Luo +3 authors
A decoding strategy that contrasts logits from different transformer layers reduces hallucinations in large language models without external knowledge or fine-tuning.
Aohan Zeng, Mingdao Liu, Rui Lu +4 authors
AgentTuning enhances the agent capabilities of open LLMs through instruction-tuning without compromising their general abilities, demonstrating performance comparable to commercial models on unseen tasks.
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.
Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +31 authors
INDUS, a suite of domain-specific LLMs trained on curated scientific corpora, surpasses general-purpose and existing domain-specific models on specialized NLP tasks and new scientific benchmarks.
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.
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.
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.
Wenhan Xiong, Jingyu Liu, Igor Molybog +18 authors
Long-context LLMs achieve significant advancements in handling extended contexts through continual pretraining and efficient instruction tuning, surpassing existing models on various benchmarks.
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.
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