23 citations · 59 across the 20 of their papers we have counts for
20 papers
Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning
Quanyu Long, Kai Jie Jiang, Jianda Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with reflection. Through a large-scale empirical analysis, we find that a substantial f…
AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
Junru Zhang, Lang Feng, Haoran Shi +4
Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heurist…
Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective
Zhuoyi Yang, Xu Guo, Tong Zhang +2
With this paper, we survey techniques for improving the predictive accuracy of pretrained large language models by allocating additional compute at inference time. In categorizing…
Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency
Colin Hong, Xu Guo, Anand Chaanan Singh +2
Recently, Test-Time Scaling (TTS) has gained increasing attention for improving LLM reasoning performance at test time without retraining the model. A notable TTS technique is Self…
Measuring Reasoning Utility in LLMs via Conditional Entropy Reduction
Xu Guo
Recent advancements in large language models (LLMs) often rely on generating intermediate reasoning steps to enhance accuracy. However, little work has examined how reasoning utili…
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
Junru Zhang, Lang Feng, Xu Guo +3
Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal pr…
SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Yige Xu, Xu Guo, Zhiwei Zeng +1
Test-Time Scaling (TTS) refers to approaches that improve reasoning performance by allocating extra computation during inference, without altering the model's parameters. While exi…
SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
Yige Xu, Xu Guo, Zhiwei Zeng +1
Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks by generating intermediate reasoning steps. However, most existing approaches…
Generating Synthetic Datasets for Few-shot Prompt Tuning
Xu Guo, Zilin Du, Boyang Li +1
A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, li…
RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference
Yige Xu, Xu Guo, Zhiwei Zeng +1
Large language models (LLMs) have brought a great breakthrough to the natural language processing (NLP) community, while leading the challenge of handling concurrent customer queri…
A Survey on Natural Language Counterfactual Generation
Yongjie Wang, Xiaoqi Qiu, Yu Yue +4
Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactual…
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo +4
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classe…
Gradient based Feature Attribution in Explainable AI: A Technical Review
Yongjie Wang, Tong Zhang, Xu Guo +1
The surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability, especially in high-stakes applications, such as healthcare a…
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Xu Guo, Yiqiang Chen
The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift…
Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction
Zilin Du, Haoxin Li, Xu Guo +1
The task of multimodal relation extraction has attracted significant research attention, but progress is constrained by the scarcity of available training data. One natural thought…
Training Multimedia Event Extraction With Generated Images and Captions
Zilin Du, Yunxin Li, Xu Guo +2
Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction. However, the task lacks annotated multimodal training data…
On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey
Xu Guo, Han Yu
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circ…
Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation
Xu Guo, Boyang Li, Han Yu
Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP t…
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
Xu Guo, Boyang Li, Han Yu +1
The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality. The adversarial neural transfer (ANT) framework utilize…
Federated Learning for Personalized Humor Recognition
Xu Guo, Han Yu, Boyang Li +5
Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challeng…