research-papers

research-papers

ChatGPT4PCG 2 Competition: Prompt Engineering for Science Birds Level Generation

Original Paper: https://arxiv.org/abs/2403.02610 By: Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro, Yi Xia, Pratch Suntichaikul, Ruck Thawonmas, Julian Togelius, Jochen Renz Abstract: This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the

research-papers

Soft-prompt Tuning for Large Language Models to Evaluate Bias

Original Paper: https://arxiv.org/abs/2306.04735 By: Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab, Deval Pandya, Laleh Seyyed-Kalantari, Faiza Khan Khattak Abstract: Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data.

research-papers

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

Original Paper: https://arxiv.org/abs/2305.13655 By: Long Lian, Boyi Li, Adam Yala, Trevor Darrell Abstract: Recent advancements in text-to-image diffusion models have yielded impressive results in generating realistic and diverse images. However, these models still struggle with complex prompts, such as those that involve numeracy and spatial

research-papers

GPT-4 Technical Report

Original Paper: https://arxiv.org/abs/2303.08774 By: OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake

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Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Original Paper: https://arxiv.org/abs/2311.16119 By: Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, Jordan Boyd-Graber Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants.

research-papers

Prompt Injection attack against LLM-integrated Applications

Original Paper: https://arxiv.org/abs/2306.05499 By: Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Zihao Wang, Xiaofeng Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu Abstract: Large Language Models (LLMs), renowned for their superior proficiency in language comprehension and generation, stimulate a vibrant ecosystem

research-papers

Inferring Properties of Graph Neural Networks

Original Paper: https://arxiv.org/abs/2401.03790 By: Dat Nguyen (1), Hieu M. Vu (2), Cong-Thanh Le (1), Bach Le (1), David Lo (3), ThanhVu Nguyen (4)Corina Pasareanu (5) ((1) University of Melbourne, (2) Independent Researcher, (3) Singapore Management University, (4) George Mason University, (5) Carnegie Mellon University)

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Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

Original Paper: https://arxiv.org/abs/2310.02304 By: Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai Abstract: Several recent advances in AI systems (e.g., Tree-of-Thoughts and Program-Aided Language Models) solve problems by providing a "scaffolding" program that structures multiple calls to language models to generate

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Consistency-guided Prompt Learning for Vision-Language Models

Original Paper: https://arxiv.org/abs/2306.01195 By: Shuvendu Roy, Ali Etemad Abstract: We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting. The basic idea of CoPrompt

research-papers

Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Original Paper: https://arxiv.org/abs/2309.08532 By: Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang Abstract: Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To

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Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Original Paper: https://arxiv.org/abs/2311.04254 By: Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang Abstract: Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences

research-papers

Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

Original Paper: https://arxiv.org/abs/2312.02119 By: Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, Amin Karbasi Abstract: While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks. In