7 citations · 23 across the 11 of their papers we have counts for
11 papers
LongSafety: Enhance Safety for Long-Context LLMs
Mianqiu Huang, Xiaoran Liu, Shaojun Zhou +11
Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for th…
Case2Code: Scalable Synthetic Data for Code Generation
Yunfan Shao, Linyang Li, Yichuan Ma +11
Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs,…
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
Xinghao Wang, Junliang He, Pengyu Wang +3
Contrastive-learning-based methods have dominated sentence representation learning. These methods regularize the representation space by pulling similar sentence representations cl…
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
Pengyu Wang, Dong Zhang, Linyang Li +5
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the…
Watermarking LLMs with Weight Quantization
Linyang Li, Botian Jiang, Pengyu Wang +3
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed. It is important to protect the model weights to avoid malicio…
PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Linyang Li, Ke Ren, Yunfan Shao +2
With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts m…
The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS
Pengyu Wang, Zhichen Ren
Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare. Ancient text di…
SeqXGPT: Sentence-Level AI-Generated Text Detection
Pengyu Wang, Linyang Li, Ke Ren +3
Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text…
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Dong Zhang, Shimin Li, Xin Zhang +4
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.…
Origin Tracing and Detecting of LLMs
Linyang Li, Pengyu Wang, Ke Ren +2
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system. More importantly, while more a…
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Yunhua Zhou, Pengyu Wang, Peiju Liu +2
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms. However, they are underdeveloped in the u…