Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

no code implementations • EMNLP 2021 • Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.

Abstractive Text Summarization Natural Language Inference +2

Instruction Induction: From Few Examples to Natural Language Task Descriptions

1 code implementation • 22 May 2022 • Or Honovich, Uri Shaham, Samuel R. Bowman, Omer Levy

Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning.

TRUE: Re-evaluating Factual Consistency Evaluation

1 code implementation • dialdoc (ACL) 2022 • Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, Yossi Matias

Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.

Question Generation Text Generation

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

1 code implementation • 16 Apr 2021 • Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.

Abstractive Text Summarization Dialogue Evaluation +3

Machine Reading of Historical Events

1 code implementation • ACL 2020 • Or Honovich, Lucas Torroba Hennigen, Omri Abend, Shay B. Cohen

Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.

Information Retrieval Reading Comprehension