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Zain Muhammad Mujahid

7 accepted papers

2025

Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts

ACL 2025finding

In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited infor…

2025

Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts

ACL 2025finding

Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies for LLM risks and safety evaluation prompts, most studies have focused on monolingual contexts, primarily in English. How…

2025

Unstructured Evidence Attribution for Long Context Query Focused Summarization

EMNLP 2025

Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query, and extracting and citing evidence spans helps improve the trustworthiness of these summaries. Whereas previous work has focused on evidence citation with fixed levels of granularity

2024

A Survey on Predicting the Factuality and the Bias of News Media

ACL 2024findings

The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. An increasing number of scholars are focusing on a coarser granularity, aiming to profile entire news…

Cited by 17SourcePDFScholar
2024

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

EMNLP 2024finding

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 present Factcheck-Bench, a holistic end-to-end framework for annotating and evaluating the factuality of LLM-generated…

2024

LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection

EMNLP 2024system demonstrations

The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains.…

2024

SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles

EMNLP 2024finding

In an era where information is quickly shared across many cultural and language contexts, the neutrality and integrity of news media are essential. Ensuring that media content remains unbiased and factual is crucial for maintaining public trust. With this in mind, we introduce SAFARI (CroSs-lingual…

Cited by 3SourcePDFScholar