7 citations · 33 across the 33 of their papers we have counts for
16 papers · 1 filter
HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
Ali Mekky, Omar El Herraoui, Preslav Nakov +1
Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bi…
A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA
Kaiyang Wan, Lang Gao, Honglin Mu +3
Multi-Hop Question Answering (MHQA) requires integrating dispersed, interdependent evidence through sequential reasoning under noise. This task is challenging for LLMs as they have…
MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation
Kareem Elozeiri, Mervat Abassy, Preslav Nakov +1
Commonsense validation evaluates whether a sentence aligns with everyday human understanding, a critical capability for developing robust natural language understanding systems. Wh…
FRaN-X: FRaming and Narratives-eXplorer
Artur Muratov, Hana Fatima Shaikh, Vanshikaa Jani +21
We present FRaN-X, a Framing and Narratives Explorer that automatically detects entity mentions and classifies their narrative roles directly from raw text. FRaN-X comprises a two-…
UnsafeChain: Enhancing Reasoning Model Safety via Hard Cases
Raj Vardhan Tomar, Preslav Nakov, Yuxia Wang
As large reasoning models (LRMs) grow more capable, chain-of-thought (CoT) reasoning introduces new safety challenges. Existing SFT-based safety alignment studies dominantly focuse…
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning
Zhuohan Xie, Daniil Orel, Rushil Thareja +22
Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA em…
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Jiahui Geng, Qing Li, Zongxiong Chen +7
The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafe…
UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking
Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan +6
The rapid adoption of Large Language Models (LLMs) has raised important concerns about the factual reliability of their outputs, particularly in low-resource languages such as Urdu…
FAID: Fine-Grained AI-Generated Text Detection Using Multi-Task Auxiliary and Multi-Level Contrastive Learning
Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang +6
The growing collaboration between humans and AI models in generative tasks has introduced new challenges in distinguishing between human-written, LLM-generated, and human-LLM colla…
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Jiahui Geng, Qing Li, Herbert Woisetschlaeger +6
This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a princi…
Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting
Fajri Koto, Rituraj Joshi, Nurdaulet Mukhituly +31
Llama-3.1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh. Sherkala-Chat (…
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
Yuxia Wang, Artem Shelmanov, Jonibek Mansurov +23
We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consis…
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh
Nurkhan Laiyk, Daniil Orel, Rituraj Joshi +4
Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. To address this, we introduce and open…
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan +11
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 f…
KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan
Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan +11
Despite having a population of twenty million, Kazakhstan's culture and language remain underrepresented in the field of natural language processing. Although large language models…
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
Yuxia Wang, Rui Xing, Jonibek Mansurov +23
Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random…