62 citations · 166 across the 19 of their papers we have counts for
20 papers
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Tao Ge, Xin Chan, Xiaoyang Wang +3
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully expl…
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
Jiaao Chen, Xiaoman Pan, Dian Yu +4
We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by comb…
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning
Zhenwen Liang, Dian Yu, Xiaoman Pan +4
Reasoning in mathematical domains remains a significant challenge for relatively small language models (LMs). Many current methods focus on specializing LMs in mathematical reasoni…
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
Pei Chen, Wenlin Yao, Hongming Zhang +4
Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and rel…
Cross-Lingual Speaker Identification Using Distant Supervision
Ben Zhou, Dian Yu, Dong Yu +1
Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-…
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models
Xiaoman Pan, Wenlin Yao, Hongming Zhang +3
Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot s…
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Zhenhailong Wang, Xiaoman Pan, Dian Yu +3
Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment…
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References
Xiang Yue, Xiaoman Pan, Wenlin Yao +3
We consider the problem of pretraining a two-stage open-domain question answering (QA) system (retriever + reader) with strong transfer capabilities. The key challenge is how to co…
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
Wenlin Yao, Xiaoman Pan, Lifeng Jin +3
Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictio…
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
Dian Yu, Kai Sun, Dong Yu +1
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this pape…
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Hongyu Gong, Yelong Shen, Dian Yu +2
In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the do…
Dialogue-Based Relation Extraction
Dian Yu, Kai Sun, Claire Cardie +1
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in…
CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang +29
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These com…
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
Hai Wang, Dian Yu, Kai Sun +2
Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely reso…
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach
Shiyang Li, Jianshu Chen, Dian Yu
Recently, pretrained language models (e.g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense rea…
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Kai Sun, Dian Yu, Dong Yu +1
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chine…
Evidence Sentence Extraction for Machine Reading Comprehension
Hai Wang, Dian Yu, Kai Sun +4
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of…
Improving Question Answering with External Knowledge
Xiaoman Pan, Kai Sun, Dian Yu +4
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-ar…
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Kai Sun, Dian Yu, Jianshu Chen +3
We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to eva…
Improving Machine Reading Comprehension with General Reading Strategies
Kai Sun, Dian Yu, Dong Yu +1
Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-c…