The rapid dissemination of information through social media and the Internet
has posed a significant challenge for fact-checking, among others in
identifying check-worthy claims that fact-checkers should pay attention to,
i.e. filtering claims needing fact-checking from a large pool of sentences.
This challenge has stressed the need to focus on determining the priority of
claims, specifically which claims are worth to be fact-checked. Despite
advancements in this area in recent years, the application of large language
models (LLMs), such as GPT, has only recently drawn attention in studies.
However, many open-source LLMs remain underexplored. Therefore, this study
investigates the application of eight prominent open-source LLMs with
fine-tuning and prompt engineering to identify check-worthy statements from
political transcriptions. Further, we propose a two-step data pruning approach
to automatically identify high-quality training data instances for effective
learning. The efficiency of our approach is demonstrated through evaluations on
the English language dataset as part of the check-worthiness estimation task of
CheckThat! 2024. Further, the experiments conducted with data pruning
demonstrate that competitive performance can be achieved with only about 44\%
of the training data. Our team ranked first in the check-worthiness estimation
task in the English language.

应用八个突出的开源语言模型（LLMs）通过精调和提示工程来从政治转录中识别值得检查的陈述，并通过两步数据修剪方法自动识别高质量训练数据实例，能够在 CheckThat！2024 的英语语言数据集方面展示有效学习和竞争性性能。

CheckThat! 2024 中的事实查找者：通过数据修剪改善 LLMs 的适用于检查的陈述检测

FactFinders at CheckThat! 2024: Refining Check-worthy Statement  Detection with LLMs through Data Pruning

A large portion of society united around the same vision and ideas carries
enormous energy. That is precisely what political figures would like to
accumulate for their cause. With this goal in mind, they can sometimes resort
to distorting or hiding the truth, unintentionally or on purpose, which opens
the door for misinformation and disinformation. Tools for automatic detection
of check-worthy claims would be of great help to moderators of debates,
journalists, and fact-checking organizations. While previous work on detecting
check-worthy claims has focused on text, here we explore the utility of the
audio signal as an additional information source. We create a new multimodal
dataset (text and audio in English) containing 48 hours of speech. Our
evaluation results show that the audio modality together with text yields
improvements over text alone in the case of multiple speakers. Moreover, an
audio-only model could outperform a text-only one for a single speaker.

本文介绍了一种新的多模态数据集（包含英语文本和音频），用于检测清楚性主张的工具，并且结果表明，使用音频信号作为信息源可以提高检测的准确率，同时也解决了单个发言者问题。

利用音频数据检测政治辩论、演讲和采访中值得核查的说法

Detecting Check-Worthy Claims in Political Debates, Speeches, and  Interviews Using Audio Data

The wide use of social media and digital technologies facilitates sharing
various news and information about events and activities. Despite sharing
positive information misleading and false information is also spreading on
social media. There have been efforts in identifying such misleading
information both manually by human experts and automatic tools. Manual effort
does not scale well due to the high volume of information, containing factual
claims, are appearing online. Therefore, automatically identifying check-worthy
claims can be very useful for human experts. In this study, we describe our
participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and
Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing
steps and applied different models to identify whether a given text is worthy
of fact checking or not. We use the oversampling technique to balance the
dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We
also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models
for the experiments. We used BERT-m for the official submissions and our
systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English,
respectively. In further experiments, our evaluation shows that transformer
models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and
English languages where a different scenario is observed for Spanish.

本文研究了使用 transformer 模型（BERT-m 和 XLM-RoBERTa-base）对社交媒体中存在的误导性信息进行事实核查的方法，实验结果表明，在荷兰语和英语中，transformer 模型优于 SVM 和 RF 模型，但在西班牙语中，情况则不同。