“The Economic Trade-offs of Large Language Models: A Case Study” – An insightful study examining the cost and impact of large language models (LLMs) for businesses. This could redefine the balance between cost and usefulness in enterprise NLP. [Link to the paper- https://arxiv.org/pdf/2306.07402.pdf]
“A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks” – A sweeping review of transformer models in the field of AI, exploring their applications and impact across various domains, and proposing future research directions. [Link to the paper-https://arxiv.org/pdf/2306.07303.pdf]
“Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks” – Reveals an unexpected trend in the human-automation relationship, with almost half of crowd workers using LLMs to boost productivity. [Link to the paper-https://arxiv.org/pdf/2306.07899v1.pdf]
“FinGPT: Open-Source Financial Large Language Models” – The launch of an open-source financial LLM, aiming to democratize financial data and stimulate innovation in the finance sector. [Link to the paper-https://arxiv.org/pdf/2306.06031.pdf]
“Prompt Injection attack against LLM-integrated Applications” – An essential security study deconstructing the implications of prompt injection attacks on LLM-integrated applications, providing tactics for mitigation. [Link to the paper-https://arxiv.org/pdf/2306.05499.pdf]
“AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks” – A deep dive into the potential symbiosis between AutoML and LLMs, exploring how each field can push the boundaries of the other. [Link to the paper-https://arxiv.org/pdf/2306.08107.pdf]
“Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level” – Offers a novel method for detecting AI-generated plagiarism, achieving up to 94% accuracy and providing a robust solution for academic settings. [Link to the paper-https://arxiv.org/pdf/2306.08122.pdf]
“Adversarial Attacks on Large Language Model-Based System and Mitigating Strategies: A Case Study on ChatGPT” – A timely study into adversarial attacks on ChatGPT, proposing mitigating mechanisms to prevent the generation of toxic texts and the propagation of false information. [Link to the paper-https://downloads.hindawi.com/journals/scn/2023/8691095.pdf]
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