Luca Soldaini

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 9 papers; trusts 221 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 238 citations into it; #16,577 of 35,345 by trust here, #12,387 by citations
• Natural Language Processing — 100 citations into it
• Computer Vision and Pattern Recognition — 74 citations into it; #10,553 of 21,575 by trust here, #17,258 by citations
• Large Language Models — 74 citations into it
• Evaluation & Benchmarks — 59 citations into it
• Information Retrieval & Search — 28 citations into it
• Information Systems — 25 citations into it
• Prompting & Reasoning — 21 citations into it

Whom Luca Soldaini trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Jimmy Ba
6. Alex Krizhevsky
7. Jacob Devlin
8. Yann LeCun
9. Alex Graves
10. Oriol Vinyals

Who trusts Luca Soldaini overall

1. Yoshitomo Matsubara
2. Ji Xin
3. Rodrigo Nogueira
4. Jimmy J. Lin
5. Marco Levorato
6. Shikib Mehri
7. Francesco Restuccia
8. Yaoliang Yu

Papers from Luca Soldaini's perspective

Ordered by how strongly the researchers Luca Soldaini trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models (2024) — 410 citations
• OLMo: Accelerating the Science of Language Models (2024) — 700 citations
• Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations
• mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval (2025)
• TÜLU 3: Pushing Frontiers in Open Language Model Post-Training (2024) — 775 citations
• Answer Generation for Retrieval-based Question Answering Systems (2021) — 28 citations
• Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing (2020) — 100 citations
• The Cascade Transformer: an Application for Efficient Answer Sentence Selection (2020) — 48 citations
• Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning (2019) — 31 citations