19 citations · 38 across the 24 of their papers we have counts for
25 papers
Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
Guangsheng Bao, Lihua Rong, Yanbin Zhao +3
Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using add…
FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation
Guangsheng Bao, Hongbo Zhang, Han Cui +4
Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memory-based or in-context learning…
Detecting RLVR Training Data via Structural Convergence of Reasoning
Hongbo Zhang, Yue Yang, Jianhao Yan +2
Reinforcement learning with verifiable rewards (RLVR) is central to training modern reasoning models, but the undisclosed training data raises concerns about benchmark contaminatio…
Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection
Ke Sun, Guangsheng Bao, Han Cui +1
Zero-shot methods detect LLM-generated text by computing statistical signatures using a surrogate model. Existing approaches typically employ a fixed surrogate for all inputs regar…
When AI Settles Down: Late-Stage Stability as a Signature of AI-Generated Text Detection
Ke Sun, Guangsheng Bao, Han Cui +1
Zero-shot detection methods for AI-generated text typically aggregate token-level statistics across entire sequences, overlooking the temporal dynamics inherent to autoregressive g…
AI-Generated Text is Non-Stationary: Detection via Temporal Tomography
Alva West, Yixuan Weng, Minjun Zhu +4
The field of AI-generated text detection has evolved from supervised classification to zero-shot statistical analysis. However, current approaches share a fundamental limitation: t…
Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Process
Zhizhang FU, Guangsheng Bao, Hongbo Zhang +2
LLMs suffer from critical reasoning issues such as unfaithfulness, bias, and inconsistency, since they lack robust causal underpinnings and may rely on superficial correlations rat…
UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge
Yang Zhang, Cunxiang Wang, Lindong Wu +4
Pairwise evaluation of Large Language Models (LLMs) is a common paradigm, but it is prone to preference bias, where judges systematically favor certain outputs, such as their own.…
Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation
Qiji Zhou, Yifan Gong, Guangsheng Bao +5
Counterfactual reasoning is crucial for robust video understanding but remains underexplored in existing multimodal benchmarks. In this paper, we introduce \textbf{COVER} (\textbf{…
Decoupling Content and Expression: Two-Dimensional Detection of AI-Generated Text
Guangsheng Bao, Lihua Rong, Yanbin Zhao +2
The wide usage of LLMs raises critical requirements on detecting AI participation in texts. Existing studies investigate these detections in scattered contexts, leaving a systemati…
Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values
Hongbo Zhang, Han Cui, Guangsheng Bao +3
We introduce Direct Value Optimization (DVO), an innovative reinforcement learning framework for enhancing large language models in complex reasoning tasks. Unlike traditional meth…
Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection
Guangsheng Bao, Yanbin Zhao, Juncai He +1
Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection. However, curr…
CycleResearcher: Improving Automated Research via Automated Review
Yixuan Weng, Minjun Zhu, Guangsheng Bao +4
The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant prog…
NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
Cunxiang Wang, Ruoxi Ning, Boqi Pan +8
Recent advancements in Large Language Models (LLMs) have pushed the boundaries of natural language processing, especially in long-context understanding. However, the evaluation of…
How Likely Do LLMs with CoT Mimic Human Reasoning?
Guangsheng Bao, Hongbo Zhang, Cunxiang Wang +2
Chain-of-thought emerges as a promising technique for eliciting reasoning capabilities from Large Language Models (LLMs). However, it does not always improve task performance or ac…
Supervised Knowledge Makes Large Language Models Better In-context Learners
Linyi Yang, Shuibai Zhang, Zhuohao Yu +8
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded th…
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng +2
Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI sys…
Token-Level Fitting Issues of Seq2seq Models
Guangsheng Bao, Zhiyang Teng, Yue Zhang
Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained wit…
Target-Side Augmentation for Document-Level Machine Translation
Guangsheng Bao, Zhiyang Teng, Yue Zhang
Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious p…
Non-Autoregressive Document-Level Machine Translation
Guangsheng Bao, Zhiyang Teng, Hao Zhou +2
Non-autoregressive translation (NAT) models achieve comparable performance and superior speed compared to auto-regressive translation (AT) models in the context of sentence-level m…
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Guangsheng Bao, Zebin Ou, Yue Zhang
Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abs…
A General Contextualized Rewriting Framework for Text Summarization
Guangsheng Bao, Yue Zhang
The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive m…
G-Transformer for Document-level Machine Translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng +2
Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we furthe…
Contextualized Rewriting for Text Summarization
Guangsheng Bao, Yue Zhang
Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness a…