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20212024
most citedFairness-guided Few-shot Prompting for Large Language Models

24 citations · 93 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CR20241 cited

Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation

Haoyu Wang, Bingzhe Wu, Yatao Bian +3

Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malic…

cs.CL202413 cited

LLM Inference Unveiled: Survey and Roofline Model Insights

Zhihang Yuan, Yuzhang Shang, Yang Zhou +11

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…

cs.CL20236 cited

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection

Tao Yang, Tianyuan Shi, Fanqi Wan +4

Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in person…

cs.CY2023

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

Qichao Wang, Tian Bian, Yian Yin +6

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial pro…

cs.CL2023

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators

Liang Chen, Yang Deng, Yatao Bian +4

Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. However, communit…

cs.AI2023

Is GPT4 a Good Trader?

Bingzhe Wu

Recently, large language models (LLMs), particularly GPT-4, have demonstrated significant capabilities in various planning and reasoning tasks \cite{cheng2023gpt4,bubeck2023sparks}…