21 citations · 63 across the 17 of their papers we have counts for
19 papers
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…
Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control
Jinyan Su, Jennifer Healey, Preslav Nakov +1
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to mitigate large language model (LLM) hallucinations by incorporating external knowledge retrieval. However…
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…
Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks
Jinyan Su, Jin Peng Zhou, Zhengxin Zhang +2
Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to mitigate LLM hallucinations and enhance their performance in knowledge-intensive domains. Howev…
Learning from Streaming Data when Users Choose
Jinyan Su, Sarah Dean
In digital markets comprised of many competing services, each user chooses between multiple service providers according to their preferences, and the chosen service makes use of th…
Corpus Poisoning via Approximate Greedy Gradient Descent
Jinyan Su, Preslav Nakov, Claire Cardie
Dense retrievers are widely used in information retrieval and have also been successfully extended to other knowledge intensive areas such as language models, e.g., Retrieval-Augme…
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…
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
Jinyan Su, Peilin Yu, Jieyu Zhang +1
Prompted weak supervision (PromptedWS) applies pre-trained large language models (LLMs) as the basis for labeling functions (LFs) in a weak supervision framework to obtain large la…
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…
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
Jinyan Su, Changhong Zhao, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general spaces. Specifically, we focus on three…
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
Jinyan Su, Jinhui Xu, Di Wang
In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexit…
Cooperation, Retaliation and Forgiveness in Revision Games
Dong Hao, Qi Shi, Jinyan Su +1
Revision game is a very new model formulating the real-time situation where players dynamically prepare and revise their actions in advance before a deadline when payoffs are reali…
Faster Rates of Private Stochastic Convex Optimization
Jinyan Su, Lijie Hu, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) and provide excess population risks for some special classes of functions th…