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20232025
most citedA Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

18 citations · 43 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

Many-Turn Jailbreaking

Xianjun Yang, Liqiang Xiao, Shiyang Li +5

Current jailbreaking work on large language models (LLMs) aims to elicit unsafe outputs from given prompts. However, it only focuses on single-turn jailbreaking targeting one speci…

cs.CL2024★ 2 cited

CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy

Mian Zhang, Xianjun Yang, Xinlu Zhang +6

There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models…

cs.CL2024★ 18 cited

A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang +7

In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: dom…

cs.CL2024★ 2 cited

Weak-to-Strong Jailbreaking on Large Language Models

Xuandong Zhao, Xianjun Yang, Tianyu Pang +4

Large language models (LLMs) are vulnerable to jailbreak attacks - resulting in harmful, unethical, or biased text generations. However, existing jailbreaking methods are computati…

cs.CL2023★ 7 cited

A Survey on Detection of LLMs-Generated Content

Xianjun Yang, Liangming Pan, Xuandong Zhao +4

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of s…

cs.CL2023★ 3 cited

Zero-Shot Detection of Machine-Generated Codes

Xianjun Yang, Kexun Zhang, Haifeng Chen +3

This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…

cs.CL2023★ 10 cited

Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models

Xianjun Yang, Xiao Wang, Qi Zhang +4

Warning: This paper contains examples of harmful language, and reader discretion is recommended. The increasing open release of powerful large language models (LLMs) has facilitate…