3 citations · 3 across the 12 of their papers we have counts for
13 papers
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning
Huaiwen Pang, Jiale Han, Junying Chen +2
Legal question answering requires answers that are not only plausible, but also grounded in legally valid and citable evidence. Standard retrieval-augmented generation often relies…
SimDiff: Depth Pruning via Similarity and Difference
Yuli Chen, Shuhao Zhang, Fanshen Meng +4
Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standard for this identification pr…
Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models
Shuhao Zhang, Yuli Chen, Jiale Han +2
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text. Correspondingly, stealing watermark algorith…
Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction
Xiang Li, Jiabao Gao, Sipei Lin +5
The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether th…
DLP: Dynamic Layerwise Pruning in Large Language Models
Yuli Chen, Bo Cheng, Jiale Han +3
Pruning has recently been widely adopted to reduce the parameter scale and improve the inference efficiency of Large Language Models (LLMs). Mainstream pruning techniques often rel…
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue
Xiang Li, Duyi Pan, Hongru Xiao +5
Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual a…
CEFW: A Comprehensive Evaluation Framework for Watermark in Large Language Models
Shuhao Zhang, Bo Cheng, Jiale Han +4
Text watermarking provides an effective solution for identifying synthetic text generated by large language models. However, existing techniques often focus on satisfying specific…
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
Xiang Li, Fan Bu, Ambuj Mehrish +4
Neural Text-to-Speech (TTS) systems find broad applications in voice assistants, e-learning, and audiobook creation. The pursuit of modern models, like Diffusion Models (DMs), hold…
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors
Shengkun Ma, Jiale Han, Yi Liang +1
Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few label…
A Robust Multi-Item Auction Design with Statistical Learning
Jiale Han, Xiaowu Dai
We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credi…
Generative Prompt Tuning for Relation Classification
Jiale Han, Shuai Zhao, Bo Cheng +2
Using prompts to explore the knowledge contained within pre-trained language models for downstream tasks has now become an active topic. Current prompt tuning methods mostly conver…
Exploring Task Difficulty for Few-Shot Relation Extraction
Jiale Han, Bo Cheng, Wei Lu
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such…
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Xu Wang, Shuai Zhao, Bo Cheng +5
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most o…