Computing Answers to Complex Questions in Broad Domains

Informacje na temat projektu

DELPHI

Identyfikator umowy o grant: 802800

DOI

10.3030/802800

Projekt został zamknięty

Data podpisania przez KE 14 Listopada 2018

Data rozpoczęcia 1 Kwietnia 2019

Data zakończenia 31 Marca 2026

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 499 375,00

Wkład UE

€ 1 499 375,00

Koordynowany przez

TEL AVIV UNIVERSITY
Israel

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Publikacje

Conference proceedings (31)

Diverse Demonstrations Improve In-context Compositional Generalization

Autorzy: Itay Levy, Ben Bogin, Jonathan Berant
Opublikowane w: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023, Strona(/y) 1401-1422
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2023.acl-long.78

MAKING RETRIEVAL-AUGMENTED LANGUAGEMODELS ROBUST TO IRRELEVANT CONTEXT

Autorzy: Ori Yoran Tomer Wolfson Ori Ram Jonathan Berant
Opublikowane w: Numer Published as a conference paper at ICLR 2024, 2024
Wydawca: The Twelfth International Conference on Learning Representations ICLR 2024

Unobserved Local Structures Make Compositional Generalization Hard

Autorzy: Ben Bogin, Shivanshu Gupta, Jonathan Berant
Opublikowane w: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2026, Strona(/y) 2731-2747
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.emnlp-main.175

"""COVR: A test-bed for Visually Grounded Compositional Generalization with real images"""

Autorzy: Ben Bogin, Shivanshu Gupta, Matt Gardner, Jonathan Berant
Opublikowane w: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021, Strona(/y) 9824–9846
Wydawca: Association for Computational Linguistics

MultimodalQA: Complex Question Answering over Text, Tables and Images

Autorzy: Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, Jonathan Berant
Opublikowane w: Proceedings of the 2021 International Conference on Learning Representations, 2021
Wydawca: ICLR 2021

A Simple and Effective Model for Answering Multi-span Questions

Autorzy: Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan Berant
Opublikowane w: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, Strona(/y) 3074-3080
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2020.emnlp-main.248

What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models

Autorzy: Mor Geva, Uri Katz, Aviv Ben-Arie, Jonathan Berant
Opublikowane w: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Wydawca: Association for Computational Linguistics

Achieving Model Robustness Through Discrete Adversarial Training

Autorzy: Maor Ivgi and Jonathan Berant
Opublikowane w: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Wydawca: ACL 2021

Learning To Retrieve Prompts for In-Context Learning

Autorzy: Ohad Rubin, Jonathan Herzig, Jonathan Berant
Opublikowane w: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022, Strona(/y) 2655-2671
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.naacl-main.191

Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors

Autorzy: Ido Amos, Jonathan Berant, Ankit Gupta
Opublikowane w: ICLR 2024 oral, 2024
Wydawca: Published as a conference paper at ICLR 2024

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments

Autorzy: Maor Ivgi, Yair Carmon, Jonathan Berant
Opublikowane w: Findings of the Association for Computational Linguistics: EMNLP 2022, 2026, Strona(/y) 7354-7371
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.findings-emnlp.544

Inferring Implicit Relations in Complex Questions with Language Models

Autorzy: Uri Katz, Mor Geva, Jonathan Berant
Opublikowane w: Findings of the Association for Computational Linguistics: EMNLP 2022, 2026, Strona(/y) 2548-2566
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.findings-emnlp.188

Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization

Autorzy: Inbar Oren, Jonathan Herzig, Jonathan Berant
Opublikowane w: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Wydawca: Association for Computational Linguistics

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

Autorzy: Ori Yoran, Alon Talmor, Jonathan Berant
Opublikowane w: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, Strona(/y) 6016-6031
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.acl-long.416

Training Vision-Language Models with Less Bimodal Supervision

Autorzy: Elad Segal, Ben Bogin, Jonathan Berant
Opublikowane w: 2022
Wydawca: AKBC 2022
DOI: 10.48550/arxiv.2211.00262

Injecting Numerical Reasoning Skills into Language Models

Autorzy: Mor Geva, Ankit Gupta, Jonathan Berant
Opublikowane w: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, Strona(/y) 946-958
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2020.acl-main.89

SCROLLS: Standardized CompaRison Over Long Language Sequences

Autorzy: Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy
Opublikowane w: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2026, Strona(/y) 12007-12021
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.emnlp-main.823

Scene Graph tO Image Generation with Contextualized Object Layout Refinement

Autorzy: Maor Ivgi, Yaniv Benny, Avichai Ben-David, Jonathan Berant, Lior Wolf
Opublikowane w: 2021 IEEE International Conference on Image Processing (ICIP), 2021, Strona(/y) 2428-2432, ISBN 978-1-6654-4115-5
Wydawca: IEEE
DOI: 10.1109/icip42928.2021.9506651

Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

Autorzy: Talmor, Alon; Tafjord, Oyvind; Clark, Peter; Goldberg, Yoav; Berant, Jonathan
Opublikowane w: Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), Numer 4, 2020
Wydawca: NeurIPS 2020

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification

Autorzy: Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant
Opublikowane w: Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (Datasets and Benchmarks Track), 2021
Wydawca: NeurIPS 2021

Improving Compositional Generalization in Semantic Parsing

Autorzy: Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, Jonathan Berant
Opublikowane w: Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, Strona(/y) 2482-2495
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2020.findings-emnlp.225

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

Autorzy: Ohad Rubin, Jonathan Berant
Opublikowane w: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, Strona(/y) 311-324
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2021.naacl-main.29

In-Context Learning with Long-Context Models: An In-Depth Exploration

Autorzy: Amanda Bertsch, Maor Ivgi, Emily Xiao, Uri Alon, Jonathan Berant, Matthew R. Gormley, Graham Neubig
Opublikowane w: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2025, Strona(/y) 12119-12149
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2025.naacl-long.605

Evaluating the Evaluation of Diversity in Natural Language Generation

Autorzy: Guy Tevet, Jonathan Berant
Opublikowane w: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021, Strona(/y) 326-346
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2021.eacl-main.25

ASSISTANTBENCH: Can Web Agents SolveRealistic and Time-Consuming Tasks?

Autorzy: Ori Yoran Samuel Joseph Amouyal Chaitanya Malaviya Ben Bogin Ofir Press Jonathan Berant
Opublikowane w: 2024
Wydawca: arXiv:2407.15711v2 [cs.CL] 21 Oct 2024

Evaluating Models’ Local Decision Boundaries via Contrast Sets

Autorzy: Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
Opublikowane w: Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, Strona(/y) 1307-1323
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2020.findings-emnlp.117

Span-based Semantic Parsing for Compositional Generalization

Autorzy: Jonathan Herzig, Jonathan Berant
Opublikowane w: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021, Strona(/y) 908-921
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2021.acl-long.74

Differentiable Scene Graphs

Autorzy: Moshiko Raboh, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson
Opublikowane w: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, Strona(/y) 1477-1486, ISBN 978-1-7281-6553-0
Wydawca: IEEE
DOI: 10.1109/wacv45572.2020.9093297

Obtaining Faithful Interpretations from Compositional Neural Networks

Autorzy: Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner
Opublikowane w: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, Strona(/y) 5594-5608
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2020.acl-main.495

Weakly Supervised Text-to-SQL Parsing through Question Decomposition

Autorzy: Tomer Wolfson, Daniel Deutch, Jonathan Berant
Opublikowane w: Findings of the Association for Computational Linguistics: NAACL 2022, 2022, Strona(/y) 2528-2542
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2022.findings-naacl.193

Answering Questions by Meta-Reasoning over Multiple Chains of Thought

Autorzy: Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan Berant
Opublikowane w: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, Strona(/y) 5942-5966
Wydawca: Association for Computational Linguistics
DOI: 10.18653/v1/2023.emnlp-main.364

Other (3)

Question Decomposition with Dependency Graphs

Autorzy: Matan Hasson, Jonathan Berant
Opublikowane w: AKBC 2021, 2021
Wydawca: non-archival

Efficient Long-Text Understanding with Short-Text Models

Autorzy: Maor Ivgi, Uri Shaham, Jonathan Berant
Opublikowane w: arXiv:2208.00748v3 [cs.CL] 27 Dec 2022, 2022
Wydawca: Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2023.
DOI: 10.48550/arxiv.2208.00748

Simplifying and Understanding State Space Modelswith Diagonal Linear RNNs

Autorzy: Ankit Gupta, Harsh Mehta, Jonathan Berant
Opublikowane w: 2022
Wydawca: OTHER
DOI: 10.48550/arxiv.2212.00768

Peer reviewed articles (6)

Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval

Autorzy: Ohad Rubin, Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics, Numer 12, 2024, Strona(/y) 1197-1213, ISSN 2307-387X
Wydawca: MIT Press
DOI: 10.1162/tacl_a_00693

oLMpics-On What Language Model Pre-training Captures

Autorzy: Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics, Numer 8, 2020, Strona(/y) 743-758, ISSN 2307-387X
Wydawca: The MIT Press
DOI: 10.1162/tacl_a_00342

Break It Down: A Question Understanding Benchmark

Autorzy: Tomer Wolfson; Mor Geva; Ankit Gupta; Matt Gardner; Yoav Goldberg; Daniel Deutch; Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics, Vol 8, Pp 183-198 (2020), Numer 2, 2020, ISSN 2307-387X
Wydawca: MIT Press Journals
DOI: 10.1162/tacl_a_00309

Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition

Autorzy: Mor Geva, Tomer Wolfson, Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics, 2021, 2021, ISSN 2307-387X
Wydawca: MIT Press

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

Autorzy: Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics, Numer 9, 2021, Strona(/y) 346-361, ISSN 2307-387X
Wydawca: MIT Press
DOI: 10.1162/tacl_a_00370

Latent compositional representations improve systematic generalization in grounded question answering.

Autorzy: Ben Bogin, Sanjay Subramanian, Matt Gardner, Jonathan Berant
Opublikowane w: Transactions of the Association for Computational Linguistics (TACL) - volume 9, 2021, ISSN 2307-387X
Wydawca: MIT Press