21 citations · 63 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
Jinyan Su, Jennifer Healey, Preslav Nakov +1
Large language models (LLMs) are increasingly optimized for long reasoning, under the assumption that more reasoning leads to better performance. However, emerging evidence suggest…
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…
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…
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12
We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtask…
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +11
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its pot…
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…
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…
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…
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12
Large language models (LLMs) have demonstrated remarkable capability to generate fluent responses to a wide variety of user queries. However, this has also raised concerns about th…