18 citations · 45 across the 9 of their papers we have counts for
11 papers
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Ella Neeman, Roee Aharoni, Or Honovich +3
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, a…
A Dataset for Sentence Retrieval for Open-Ended Dialogues
Itay Harel, Hagai Taitelbaum, Idan Szpektor +1
We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next…
All You May Need for VQA are Image Captions
Soravit Changpinyo, Doron Kukliansky, Idan Szpektor +3
Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we…
TRUE: Re-evaluating Factual Consistency Evaluation
Or Honovich, Roee Aharoni, Jonathan Herzig +7
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may h…
What's the best place for an AI conference, Vancouver or ______: Why completing comparative questions is difficult
Avishai Zagoury, Einat Minkov, Idan Szpektor +1
Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. He…
: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Or Honovich, Leshem Choshen, Roee Aharoni +3
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 t…