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。
Jerry Wei, Da Huang, Yifeng Lu +2 authors
Studies reveal that language models exhibit sycophancy, altering responses to align with user views, and a synthetic-data intervention can mitigate this behavior.
Quan Sun, Qiying Yu, Yufeng Cui +7 authors
Emu, a Transformer-based multimodal model, generates images and texts in various contexts and demonstrates superior performance across zero-shot and few-shot tasks compared to existing models.
Vipul Raheja, Dhruv Kumar, Ryan Koo +1 authors
CoEdIT, a fine-tuned large language model, enhances text editing quality and efficiency with diverse instructions, achieving state-of-the-art performance and user preference.
Bo Li, Yuanhan Zhang, Liangyu Chen +3 authors
Multimodal instruction tuning improves the performance of models like Otter, based on OpenFlamingo, with an optimized dataset and reduced training resource requirements.
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