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Original Paper: https://arxiv.org/abs/2402.11140 By: Sijia Chen, Baochun Li, Di Niu Abstract: The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work,
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According to the latest and greatest research, the SoTA eval techniques use LLMs as evaluators. It's a little meta, but it works better than anything else. But sometimes, they are too expensive to run in production. Here's how Athina solves this. How to run LLM-graded evals
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Original Paper: https://arxiv.org/abs/2402.07408 By: Mingrui Ma, Lansheng Han, Chunjie Zhou Abstract: The frequent occurrence of cyber-attacks has made webshell attacks and defense gradually become a research hotspot in the field of network security. However, the lack of publicly available benchmark datasets and the over-reliance on
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Original Paper: https://arxiv.org/abs/2402.07812 By: Thomas Pouplin, Hao Sun, Samuel Holt, Mihaela van der Schaar Abstract: Large Language Models (LLMs) have demonstrated their strong ability to assist people and show "sparks of intelligence". However, several open challenges hinder their wider application: such as concerns
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Original Paper: https://arxiv.org/abs/2306.01798 By: David James Woo, Kai Guo, Hengky Susanto Abstract: This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative artificial intelligence (AI) tools during short story writing. Sixty-seven Hong Kong secondary school students created generative-AI
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Original Paper: https://arxiv.org/abs/2309.17179 By: Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, Jun Wang Abstract: Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step
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Original Paper: https://arxiv.org/abs/2402.06738 By: Yifan Ding, Amrit Poudel, Qingkai Zeng, Tim Weninger, Balaji Veeramani, Sanmitra Bhattacharya Abstract: The ability of Large Language Models (LLMs) to generate factually correct output remains relatively unexplored due to the lack of fact-checking and knowledge grounding during training and inference.
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Original Paper: https://arxiv.org/abs/2402.07927 By: Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha Abstract: Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known
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Original Paper: https://arxiv.org/abs/2402.02420 By: Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Jyoti Das, Preslav Nakov Abstract: Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting,
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Original Paper: https://arxiv.org/abs/2401.14447 By: Zijie J. Wang, Aishwarya Chakravarthy, David Munechika, Duen Horng Chau Abstract: Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While
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Original Paper: https://arxiv.org/abs/2401.16541 By: Sabur Butt, Fazlourrahman Balouchzahi, Abdul Gafar Manuel Meque, Maaz Amjad, Hector G. Ceballos Cancino, Grigori Sidorov, Alexander Gelbukh Abstract: The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions
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Original Paper: https://arxiv.org/abs/2401.16380 By: Pratyush Maini, Skyler Seto, He Bai, David Grangier, Yizhe Zhang, Navdeep Jaitly Abstract: Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data
