29 citations · 93 across the 13 of their papers we have counts for
13 papers
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
Zhexin Zhang, Yida Lu, Jingyuan Ma +8
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs'…
Unveiling the Implicit Toxicity in Large Language Models
Jiaxin Wen, Pei Ke, Hao Sun +4
The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent st…
Safety Assessment of Chinese Large Language Models
Hao Sun, Zhexin Zhang, Jiawen Deng +2
With the rapid popularity of large language models such as ChatGPT and GPT-4, a growing amount of attention is paid to their safety concerns. These models may generate insulting an…
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements
Jiawen Deng, Jiale Cheng, Hao Sun +2
As generative large model capabilities advance, safety concerns become more pronounced in their outputs. To ensure the sustainable growth of the AI ecosystem, it's imperative to un…
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation
Zhexin Zhang, Jiale Cheng, Hao Sun +5
Large pretrained language models can easily produce toxic or biased content, which is prohibitive for practical use. In order to detect such toxic generations, existing methods rel…
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions
Hao Sun, Zhexin Zhang, Fei Mi +6
Morality in dialogue systems has raised great attention in research recently. A moral dialogue system aligned with users' values could enhance conversation engagement and user conn…
PAL: Persona-Augmented Emotional Support Conversation Generation
Jiale Cheng, Sahand Sabour, Hao Sun +2
Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effect…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Yuxian Gu, Jiaxin Wen, Hao Sun +8
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversationa…
COLD: A Benchmark for Chinese Offensive Language Detection
Jiawen Deng, Jingyan Zhou, Hao Sun +4
Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is s…
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
Hao Sun, Guangxuan Xu, Jiawen Deng +6
Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently. However, dialogue safety pro…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…
PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support
Hao Sun, Zhenru Lin, Chujie Zheng +2
Great research interests have been attracted to devise AI services that are able to provide mental health support. However, the lack of corpora is a main obstacle to this research,…