Athina AI

research-papers

PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification

Original Paper: https://arxiv.org/abs/2308.02816 By: Hongwei Yao, Jian Lou, Kui Ren, Zhan Qin Abstract: Large language models (LLMs) have witnessed a meteoric rise in popularity among the general public users over the past few months, facilitating diverse downstream tasks with human-level accuracy and proficiency. Prompts play

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How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings

Original Paper: https://arxiv.org/abs/2305.11853 By: Shuaichen Chang, Eric Fosler-Lussier Abstract: Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those

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Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination

Original Paper: https://arxiv.org/abs/2311.15548 By: Haoqiang Kang, Xiao-Yang Liu Abstract: The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite the growing concerns, there has been a lack of empirical

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Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions

Original Paper: https://arxiv.org/abs/2311.13982 By: Shulin Cao, Jiajie Zhang, Jiaxin Shi, Xin Lv, Zijun Yao, Qi Tian, Juanzi Li, Lei Hou Abstract: Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought (CoT) reasoning. However, they tend to generate factually incorrect reasoning steps

research-papers

Prompt a Robot to Walk with Large Language Models

Original Paper: https://arxiv.org/abs/2309.09969 By: Yen-Jen Wang, Bike Zhang, Jianyu Chen, Koushil Sreenath Abstract: Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. Recently, there has been escalating interest in deploying LLMs for robotics, aiming to harness the power

research-papers

To be or not to be? an exploration of continuously controllable prompt engineering

Original Paper: https://arxiv.org/abs/2311.09773 By: Yuhan Sun, Mukai Li, Yixin Cao, Kun Wang, Wenxiao Wang, Xingyu Zeng, Rui Zhao Abstract: As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for customizing models

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Fine-tuning Language Models for Factuality

Original Paper: https://arxiv.org/abs/2311.08401 By: Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, Chelsea Finn Abstract: The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are

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Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

Original Paper: https://arxiv.org/abs/2305.15778 By: Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, Jun Zeng, Supriyo Ghosh, Xuchao Zhang, Chaoyun Zhang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Tianyin Xu Abstract: Ensuring the reliability and

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Promise: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models

Original Paper: https://arxiv.org/abs/2310.19721 By: Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, Ipek Oguz Abstract: To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce

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A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

Original Paper: https://arxiv.org/abs/2311.07491 By: Hejing Cao, Zhenwei An, Jiazhan Feng, Kun Xu, Liwei Chen, Dongyan Zhao Abstract: While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in

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Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning

Original Paper: https://arxiv.org/abs/2311.08505 By: Xin Su, Tiep Le, Steven Bethard, Phillip Howard Abstract: An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge,

research-papers

Testing LLMs on Code Generation with Varying Levels of Prompt Specificity

Original Paper: https://arxiv.org/abs/2311.07599 By: Lincoln Murr, Morgan Grainger, David Gao Abstract: Large language models (LLMs) have demonstrated unparalleled prowess in mimicking human-like text generation and processing. Among the myriad of applications that benefit from LLMs, automated code generation is increasingly promising. The potential to transform