#zeroshotlearning — Public Fediverse posts

Follow the authors: Serwar Basch (UKP Lab/hessian.AI/Technische Universität Darmstadt), Ilia Kuznetsov (UKP Lab/hessian.AI/Technische Universität Darmstadt), Tom Hope (The Hebrew University of Jerusalem/Ai2), and Iryna Gurevych (UKP Lab/hessian.AI/Technische Universität Darmstadt)

And say hi to us if you are also in Rabat! #EACL2026

#NLProc #UKPLab #NLP #LLMs #AI
#MultiDocumentReasoning #ZeroShotLearning #Evaluation #OpenScience #ComputerScience

Follow the authors: Serwar Basch (UKP Lab/hessian.AI/Technische Universität Darmstadt), Ilia Kuznetsov (UKP Lab/hessian.AI/Technische Universität Darmstadt), Tom Hope (The Hebrew University of Jerusalem/Ai2), and Iryna Gurevych (UKP Lab/hessian.AI/Technische Universität Darmstadt)

And say hi to us if you are also in Rabat! #EACL2026

#NLProc #UKPLab #NLP #LLMs #AI
#MultiDocumentReasoning #ZeroShotLearning #Evaluation #OpenScience #ComputerScience

Follow the authors: Serwar Basch (UKP Lab/hessian.AI/Technische Universität Darmstadt), Ilia Kuznetsov (UKP Lab/hessian.AI/Technische Universität Darmstadt), Tom Hope (The Hebrew University of Jerusalem/Ai2), and Iryna Gurevych (UKP Lab/hessian.AI/Technische Universität Darmstadt)

And say hi to us if you are also in Rabat! #EACL2026

#NLProc #UKPLab #NLP #LLMs #AI
#MultiDocumentReasoning #ZeroShotLearning #Evaluation #OpenScience #ComputerScience

Follow the authors: Serwar Basch (UKP Lab/hessian.AI/Technische Universität Darmstadt), Ilia Kuznetsov (UKP Lab/hessian.AI/Technische Universität Darmstadt), Tom Hope (The Hebrew University of Jerusalem/Ai2), and Iryna Gurevych (UKP Lab/hessian.AI/Technische Universität Darmstadt)

And say hi to us if you are also in Rabat! #EACL2026

#NLProc #UKPLab #NLP #LLMs #AI
#MultiDocumentReasoning #ZeroShotLearning #Evaluation #OpenScience #ComputerScience

This research introduces Open-YOLO 3D, a novel method using 2D object detectors for high-speed, open-vocabulary 3D instance segmentation. https://hackernoon.com/a-new-approach-to-3d-scene-understanding-replacing-heavy-segmentation-models-for-a-16x-speedup #zeroshotlearning

We tested GPT-4, Llama-2, and more on symbolic puzzles—see why even the strongest LLMs fail without fine-tuning. https://hackernoon.com/why-llms-struggle-with-arithmetic-puzzles #zeroshotlearning

Can LLMs solve math? This study explores puzzles, synthetic data, and fine-tuning to push AI’s limits in reasoning and extrapolation. https://hackernoon.com/testing-large-language-models-on-math-puzzles #zeroshotlearning

Fine-tuning boosts AI reasoning: models trained on 100M samples achieve higher pass@1 rates in puzzle-solving across in-distribution and OOD tests. https://hackernoon.com/evaluating-fine-tuned-llms-on-reasoning-puzzles #zeroshotlearning

A new dataset turns arithmetic puzzles into a benchmark for AI, testing LLaMA’s reasoning with LoRA fine-tuning and OOD evaluation. https://hackernoon.com/a-framework-for-synthesizing-arithmetical-puzzle-datasets-for-large-language-models #zeroshotlearning

Fine-tuning LLMs with synthetic data boosts multi-step mathematical reasoning and improves zero-shot performance on novel benchmarks. https://hackernoon.com/how-llms-learn-to-solve-complex-math #zeroshotlearning