Articles for category: AI Research

[2503.09701] Have LLMs Made Active Learning Obsolete? Surveying the NLP Community

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[2503.05491] Statistical Deficiency for Task Inclusion Estimation

[Submitted on 7 Mar 2025 (v1), last revised 13 Mar 2025 (this version, v2)] View a PDF of the paper titled Statistical Deficiency for Task Inclusion Estimation, by Lo\”ic Fosse and Fr\’ed\’eric B\’echet and Beno\^it Favre and G\’eraldine Damnati and Gw\’enol\’e Lecorv\’e and Maxime Darrin and Philippe Formont and Pablo Piantanida View PDF HTML (experimental) Abstract:Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any task. Even though transfer learning and multitask learning try to leverage the underlying task

Google and Kaggle’s GenAI Intensive live course 2025

In 2024, more than 140,000 people participated in Google and Kaggle’s Gen AI Intensive live course. Our course is returning this year, with updated content, new speakers and a Kaggle capstone project. Participants can earn a certificate and compete for prizes, and it remains free for everyone. From March 31 through April 4, 2025, Google experts will go through foundational gen AI topics like prompt engineering and embeddings, and answer your questions through daily livestreams. Coursework will include AI-generated podcasts using NotebookLM, whitepapers by Google experts and practical code labs for hands-on experience with Gemini and other services. You’ll be

Reddit – Heart of the internet

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Reddit – Heart of the internet

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Reddit – Heart of the internet

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Reddit – Heart of the internet

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Reddit – Heart of the internet

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Reddit – Heart of the internet

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Evolving tables in the reasoning chain for table understanding

After the next operation f is determined, in the second stage, we need to generate the arguments. As above, Chain-of-Table considers three components in the prompt as shown in the figure: (1) the question, (2) the selected operation and its required arguments, and (3) the latest intermediate table. For instance, when the operation f_group_by is selected, it requires a header name as its argument. The LLM selects a suitable header within the table. Equipped with the selected operation and the generated arguments, Chain-of-Table executes the operation and constructs a new intermediate table for the following reasoning. Chain-of-Table iterates the previous