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cs.CL2025

RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises

Zenan Zhai, Hao Li, Xudong Han +4

Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text contai…

cs.CL2025

SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning

Renxi Wang, Honglin Mu, Liqun Ma +5

Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchma…

cs.CL2025

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…

cs.CL2024

Loki: An Open-Source Tool for Fact Verification

Haonan Li, Xudong Han, Hao Wang +7

We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of f…

cs.CL2024

Arabic Dataset for LLM Safeguard Evaluation

Yasser Ashraf, Yuxia Wang, Bin Gu +2

The growing use of large language models (LLMs) has raised concerns regarding their safety. While many studies have focused on English, the safety of LLMs in Arabic, with its lingu…

cs.CL2024

ToolGen: Unified Tool Retrieval and Calling via Generation

Renxi Wang, Xudong Han, Lei Ji +3

As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional method…