The rapid expansion of the digital world has propelled sentiment analysis
into a critical tool across diverse sectors such as marketing, politics,
customer service, and healthcare. While there have been significant
advancements in sentiment analysis for widely spoken languages, low-resource
languages, such as Bangla, remain largely under-researched due to resource
constraints. Furthermore, the recent unprecedented performance of Large
Language Models (LLMs) in various applications highlights the need to evaluate
them in the context of low-resource languages. In this study, we present a
sizeable manually annotated dataset encompassing 33,605 Bangla news tweets and
Facebook comments. We also investigate zero- and few-shot in-context learning
with several language models, including Flan-T5, GPT-4, and Bloomz, offering a
comparative analysis against fine-tuned models. Our findings suggest that
monolingual transformer-based models consistently outperform other models, even
in zero and few-shot scenarios. To foster continued exploration, we intend to
make this dataset and our research tools publicly available to the broader
research community. In the spirit of further research, we plan to make this
dataset and our experimental resources publicly accessible to the wider
research community.

本研究通过 33,605 条孟加拉文新闻推文和 Facebook 评论创建了一个庞大的手动注释数据集，并对 Flan-T5、GPT-4 和 Bloomz 等多种语言模型进行了零指导和少量指导的上下文学习，与精细调优模型进行了比较分析。研究结果表明，在零指导和少量指导的场景下，单语言变换器模型始终优于其他模型。为了促进进一步的研究探索，我们打算向更广泛的研究社区公开提供这个数据集和我们的研究工具。

LLM 对零样本和少样本提示的比较研究：孟加拉情感分析的微调模型

Zero- and Few-Shot Prompting with LLMs: A Comparative Study with  Fine-tuned Models for Bangla Sentiment Analysis

Text-guided image editing is widely needed in daily life, ranging from
personal use to professional applications such as Photoshop. However, existing
methods are either zero-shot or trained on an automatically synthesized
dataset, which contains a high volume of noise. Thus, they still require lots
of manual tuning to produce desirable outcomes in practice. To address this
issue, we introduce MagicBrush (this https URL),
the first large-scale, manually annotated dataset for instruction-guided real
image editing that covers diverse scenarios: single-turn, multi-turn,
mask-provided, and mask-free editing. MagicBrush comprises over 10K manually
annotated triples (source image, instruction, target image), which supports
trainining large-scale text-guided image editing models. We fine-tune
InstructPix2Pix on MagicBrush and show that the new model can produce much
better images according to human evaluation. We further conduct extensive
experiments to evaluate current image editing baselines from multiple
dimensions including quantitative, qualitative, and human evaluations. The
results reveal the challenging nature of our dataset and the gap between
current baselines and real-world editing needs.

MagicBrush 是第一个大规模手动注释的指令引导真实图像编辑数据集，包括单回合、多回合、提供掩码和自由掩码编辑等多种场景，通过 MagicBrush fine-tune InstructPix2Pix 模型并进行了人类评价，结果展示出了我们的数据集的挑战性以及当前算法与真实世界编辑需求之间的差距。

MagicBrush: 一个手动注释的数据集用于指导图像编辑

MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image  Editing

Lexical Semantic Change detection, i.e., the task of identifying words that
change meaning over time, is a very active research area, with applications in
NLP, lexicography, and linguistics. Evaluation is currently the most pressing
problem in Lexical Semantic Change detection, as no gold standards are
available to the community, which hinders progress. We present the results of
the first shared task that addresses this gap by providing researchers with an
evaluation framework and manually annotated, high-quality datasets for English,
German, Latin, and Swedish. 33 teams submitted 186 systems, which were
evaluated on two subtasks.

第一项共享任务的结果，提供了评估框架和高质量的英语，德语，拉丁语和瑞典语手动注释数据集，解决了词汇语义变化检测领域中评估问题的压力，并吸引了 33 个团队提交了 186 个系统。