In this paper, we extend financial sentiment analysis~(FSA) to event-level
since events usually serve as the subject of the sentiment in financial text.
Though extracting events from the financial text may be conducive to accurate
sentiment predictions, it has specialized challenges due to the lengthy and
discontinuity of events in a financial text. To this end, we reconceptualize
the event extraction as a classification task by designing a categorization
comprising coarse-grained and fine-grained event categories. Under this
setting, we formulate the \textbf{E}vent-Level \textbf{F}inancial
\textbf{S}entiment \textbf{A}nalysis~(\textbf{EFSA} for short) task that
outputs quintuples consisting of (company, industry, coarse-grained event,
fine-grained event, sentiment) from financial text. A large-scale Chinese
dataset containing $12,160$ news articles and $13,725$ quintuples is publicized
as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based
approach is devised for this task. Systematically investigations are conducted
on our dataset, and the empirical results demonstrate the benchmarking scores
of existing methods and our proposed method can reach the current
state-of-the-art. Our dataset and framework implementation are available at
this https URL

该研究将金融情感分析（FSA）扩展到事件级别，通过设计粗细分类的事件类别，将事件从金融文本中提取出来，并提出了一个涵盖（公司，行业，粗事件，细事件，情感）的事件级别金融情感分析（EFSA）任务，在一个大规模的中文数据集上得到了与现有方法相媲美的结果。

EFSA: 事件级金融情感分析

EFSA: Towards Event-Level Financial Sentiment Analysis

Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced the comprehension of multimedia content, bringing
together diverse modalities such as text, images, and videos. However, a
critical challenge faced by these models, especially when processing video
inputs, is the occurrence of hallucinations - erroneous perceptions or
interpretations, particularly at the event level. This study introduces an
innovative method to address event-level hallucinations in MLLMs, focusing on
specific temporal understanding in video content. Our approach leverages a
novel framework that extracts and utilizes event-specific information from both
the event query and the provided video to refine MLLMs' response. We propose a
unique mechanism that decomposes on-demand event queries into iconic actions.
Subsequently, we employ models like CLIP and BLIP2 to predict specific
timestamps for event occurrences. Our evaluation, conducted using the
Charades-STA dataset, demonstrates a significant reduction in temporal
hallucinations and an improvement in the quality of event-related responses.
This research not only provides a new perspective in addressing a critical
limitation of MLLMs but also contributes a quantitatively measurable method for
evaluating MLLMs in the context of temporal-related questions.

通过提取和利用事件查询和提供的视频中的事件特定信息，我们介绍了一种创新的方法来解决多模态大型语言模型中发生的事件级幻觉问题，重点关注视频内容中的时间理解问题，在 Charades-STA 数据集上的评估表明，该研究不仅在解决多模态大型语言模型的关键限制方面提供了新的视角，还为在与时间相关的问题上评估多模态大型语言模型提供了一种定量可测量的方法。