19 citations · 61 across the 13 of their papers we have counts for
13 papers
Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework
Xuanming Zhang, Yuxuan Chen, Yiming Zheng +3
In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require develop…
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…
Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization
Zhexin Zhang, Junxiao Yang, Pei Ke +3
While significant attention has been dedicated to exploiting weaknesses in LLMs through jailbreaking attacks, there remains a paucity of effort in defending against these attacks.…
SafetyBench: Evaluating the Safety of Large Language Models
Zhexin Zhang, Leqi Lei, Lindong Wu +7
With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an e…
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Zhexin Zhang, Jiaxin Wen, Minlie Huang
Large pre-trained language models achieve impressive results across many tasks. However, recent works point out that pre-trained language models may memorize a considerable fractio…
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…
Selecting Stickers in Open-Domain Dialogue through Multitask Learning
Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei +2
With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a co…
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Zhexin Zhang, Jiaxin Wen, Jian Guan +1
Endowing the protagonist with a specific personality is essential for writing an engaging story. In this paper, we aim to control the protagonist's persona in story generation, i.e…
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics
Jian Guan, Zhexin Zhang, Zhuoer Feng +5
Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, ex…