519 citations · 789 across the 23 of their papers we have counts for
24 papers
Instruction-tuned Language Models are Better Knowledge Learners
Zhengbao Jiang, Zhiqing Sun, Weijia Shi +6
In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued…
Learning to Filter Context for Retrieval-Augmented Generation
Zhiruo Wang, Jun Araki, Zhengbao Jiang +2
On-the-fly retrieval of relevant knowledge has proven an essential element of reliable systems for tasks such as open-domain question answering and fact verification. However, beca…
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao +6
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augme…
From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Qian Liu, Fan Zhou, Zhengbao Jiang +2
Fine-tuning language models on tasks with instructions has demonstrated potential in facilitating zero-shot generalization to unseen tasks. In this paper, we introduce a straightfo…
GPTScore: Evaluate as You Desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang +1
Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the u…
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…
SPE: Symmetrical Prompt Enhancement for Fact Probing
Yiyuan Li, Tong Che, Yezhen Wang +3
Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowle…
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…
EditEval: An Instruction-Based Benchmark for Text Improvements
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang +6
Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text. Writing, however, is naturally an iterativ…
PEER: A Collaborative Language Model
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang +7
Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process,…
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
Zhengbao Jiang, Yi Mao, Pengcheng He +2
The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value. The intrinsic complexity of handling tables oft…
DocPrompting: Generating Code by Retrieving the Docs
Shuyan Zhou, Uri Alon, Frank F. Xu +3
Publicly available source-code libraries are continuously growing and changing. This makes it impossible for models of code to keep current with all available APIs by simply traini…
Table Retrieval May Not Necessitate Table-specific Model Design
Zhiruo Wang, Zhengbao Jiang, Eric Nyberg +1
Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent wo…
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu +3
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, whic…
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction
Zhengbao Jiang, Jialong Han, Bunyamin Sisman +1
Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text rela…
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 _."…
GSum: A General Framework for Guided Neural Abstractive Summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi +2
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies a…
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Frank F. Xu, Zhengbao Jiang, Pengcheng Yin +2
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that dev…
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…
Graph-Revised Convolutional Network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang +2
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linka…
Generalizing Natural Language Analysis through Span-relation Representations
Zhengbao Jiang, Wei Xu, Jun Araki +1
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially design…
Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
Songwei Ge, Zhicheng Dou, Zhengbao Jiang +2
Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical intere…
Improving Open Information Extraction via Iterative Rank-Aware Learning
Zhengbao Jiang, Pengcheng Yin, Graham Neubig
Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extract…