163 citations · 539 across the 205 of their papers we have counts for
26 papers · 1 filter
LLM360: Towards Fully Transparent Open-Source LLMs
Zhengzhong Liu, Aurick Qiao, Willie Neiswanger +25
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs…
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
Maram Hasanain, Firoj Alam, Hamdy Mubarak +5
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text:…
Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition
Tariq Alhindi, Smaranda Muresan, Preslav Nakov
Recognizing fallacies is crucial for ensuring the quality and validity of arguments across various domains. However, computational fallacy recognition faces challenges due to the d…
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid +10
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we…
A Survey of Confidence Estimation and Calibration in Large Language Models
Jiahui Geng, Fengyu Cai, Yuxia Wang +3
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable…
A Template Is All You Meme
Luke Bates, Peter Ebert Christensen, Preslav Nakov +1
Templatic memes, characterized by a semantic structure adaptable to the creator's intent, represent a significant yet underexplored area within meme processing literature. With the…
Adapting Fake News Detection to the Era of Large Language Models
Jinyan Su, Claire Cardie, Preslav Nakov
In the age of large language models (LLMs) and the widespread adoption of AI-driven content creation, the landscape of information dissemination has witnessed a paradigm shift. Wit…
Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
Shubham Mittal, Megha Sundriyal, Preslav Nakov
Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media po…
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Liangming Pan, Xinyuan Lu, Min-Yen Kan +1
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking system…
Factuality Challenges in the Era of Large Language Models
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha +15
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incre…
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering
Saptarshi Sengupta, Connor Heaton, Shreya Ghosh +3
We study extractive question-answering in the medical domain (Medical-EQA). This problem has two main challenges: (i) domain specificity, as most AI models lack necessary domain kn…
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
Megha Sundriyal, Tanmoy Chakraborty, Preslav Nakov
With the rise of social media, users are exposed to many misleading claims. However, the pervasive noise inherent in these posts presents a challenge in identifying precise and pro…
Fake News Detectors are Biased against Texts Generated by Large Language Models
Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov +2
The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society. In the era of Large Language Models (LLMs), the capability to generate…
Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
Yuxia Wang, Haonan Li, Xudong Han +2
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging. This requires developers to be able to identify risks through t…
Rethinking STS and NLI in Large Language Models
Yuxia Wang, Minghan Wang, Preslav Nakov
Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks. However, when…
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data
Petar Ivanov, Ivan Koychev, Momchil Hardalov +1
Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers. While previou…
Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data
Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva +3
The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discov…
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
Jinyan Su, Terry Yue Zhuo, Di Wang +1
With the rapid progress of large language models (LLMs) and the huge amount of text they generated, it becomes more and more impractical to manually distinguish whether a text is m…
Fact-Checking Complex Claims with Program-Guided Reasoning
Liangming Pan, Xiaobao Wu, Xinyuan Lu +4
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Check…
QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models
Maram Hasanain, Ahmed Oumar El-Shangiti, Rabindra Nath Nandi +2
Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can…
Detecting Propaganda Techniques in Code-Switched Social Media Text
Muhammad Umar Salman, Asif Hanif, Shady Shehata +1
Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda. With the rise of social media, propaganda has…
On the Risk of Misinformation Pollution with Large Language Models
Yikang Pan, Liangming Pan, Wenhu Chen +3
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact…
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Xinyuan Lu, Liangming Pan, Qian Liu +2
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present S…
Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses
Ashraf Haddad, Najwa Aaraj, Preslav Nakov +1
In recent years, a proliferation of cyber-security threats and diversity has been on the rise culminating in an increase in their reporting and analysis. To counter that, many non-…