7 citations · 12 across the 3 of their papers we have counts for
7 papers · 1 filter
Knowledge-grounded Natural Language Recommendation Explanation
Anthony Colas, Jun Araki, Zhengyu Zhou +2
Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in…
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Zhengbao Jiang, Luyu Gao, Jun Araki +4
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and d…
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
Zhengbao Jiang, Jun Araki, Haibo Ding +1
Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving rele…
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
Zhengbao Jiang, Jun Araki, Haibo Ding +1
Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to pro…
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki +2
Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as "Punta Cana is located in _."…
How Can We Know What Language Models Know?
Zhengbao Jiang, Frank F. Xu, Jun Araki +1
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profe…