Ella Neeman — accepted AI-conference papers

2023

DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering

Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, Omri Abend

ACL 2023long

Question answering models commonly have access to two sources of “knowledge” during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e.g., a Wikipedia passage) given to the model to generate a grounded a…

Cited by 66 Source ↗PDF ↗code ↗Scholar ↗

2021

Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend

EMNLP 2021main

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. Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose a…

Cited by 191 Source ↗PDF ↗code ↗Scholar ↗