Athina AI

research-papers

Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

Original Paper: https://arxiv.org/abs/2309.13339 By: Xufeng Zhao, Mengdi Li, Wenhao Lu, Cornelius Weber, Jae Hee Lee, Kun Chu, Stefan Wermter Abstract: Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially

research-papers

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Original Paper: https://arxiv.org/abs/2310.13345 By: Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan Kankanhalli Abstract: The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This paper proposes

research-papers

Text Summarization: LLM Failure Cases and Detection Methods

Large Language Models (LLMs) excel at sifting lengthy text to extract key insights, transforming documents into concise summaries. An increasing number of companies are using LLMs for text summarization—be it to condense massive datasets or enhance data quality. Yet, ensuring the reliability and consistency of LLMs in production remains

research-papers

The Foundation Model Transparency Index

Original Paper: https://arxiv.org/abs/2310.12941 By: Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang Abstract: Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation

research-papers

NLPBench: Evaluating Large Language Models on Solving NLP Problems

Original Paper: https://arxiv.org/abs/2309.15630 By: Linxin Song, Jieyu Zhang, Lechao Cheng, Pengyuan Zhou, Tianyi Zhou, Irene Li Abstract: Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research

research-papers

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Original Paper: https://arxiv.org/abs/2310.11324 By: Melanie Sclar, Yejin Choi, Yulia Tsvetkov, Alane Suhr Abstract: As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can strongly influence model behavior,

research-papers

Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning

Original Paper: https://arxiv.org/abs/2310.11397 By: Rui Wen, Tianhao Wang, Michael Backes, Yang Zhang, Ahmed Salem Abstract: Large Language Models (LLMs) are powerful tools for natural language processing, enabling novel applications and user experiences. However, to achieve optimal performance, LLMs often require adaptation with private data, which

research-papers

Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks

Original Paper: https://arxiv.org/abs/2310.10077 By: Shuyu Jiang, Xingshu Chen, Rui Tang Abstract: Recently, Large language models (LLMs) with powerful general capabilities have been increasingly integrated into various Web applications, while undergoing alignment training to ensure that the generated content aligns with user intent and ethics. Unfortunately,

research-papers

OpenAgents: An Open Platform for Language Agents in the Wild

Original Paper: https://arxiv.org/abs/2310.10634 By: Tianbao Xie, Fan Zhou, Zhoujun Cheng, Peng Shi, Luoxuan Weng, Yitao Liu, Toh Jing Hua, Junning Zhao, Qian Liu, Che Liu, Leo Z. Liu, Yiheng Xu, Hongjin Su, Dongchan Shin, Caiming Xiong, Tao Yu Abstract: Language agents show potential in being

research-papers

Autonomous Tree-search Ability of Large Language Models

Original Paper: https://arxiv.org/abs/2310.10686 By: Zheyu Zhang, Zhuorui Ye, Yikang Shen, Chuang Gan Abstract: Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques, but they fall short on tasks that require exploration, strategic foresight, and sequential decision-making. Recent works propose to utilize

research-papers

PromptTTS 2: Describing and Generating Voices with Text Prompt

Original Paper: https://arxiv.org/abs/2309.02285 By: Yichong Leng, Zhifang Guo, Kai Shen, Xu Tan, Zeqian Ju, Yanqing Liu, Yufei Liu, Dongchao Yang, Leying Zhang, Kaitao Song, Lei He, Xiang-Yang Li, Sheng Zhao, Tao Qin, Jiang Bian Abstract: Speech conveys more information than text, as the same word

research-papers

Re-Reading Improves Reasoning in Large Language Models

Original Paper: https://arxiv.org/abs/2309.06275 By: Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou Abstract: To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i.e., \textbf{Re}