519 citations · 657 across the 17 of their papers we have counts for
18 papers
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Yang Xiao, Yi Cheng, Jinlan Fu +3
In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the la…
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…
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
Lin Xu, Qixian Zhou, Jinlan Fu +2
Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grou…
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
Yang Xiao, Jinlan Fu, See-Kiong Ng +1
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary. We approach this by first characterizing the distinguishability of datasets…
Polyglot Prompt: Multilingual Multitask PrompTraining
Jinlan Fu, See-Kiong Ng, Pengfei Liu
This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e…
DataLab: A Platform for Data Analysis and Intervention
Yang Xiao, Jinlan Fu, Weizhe Yuan +5
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.…
A Partition Filter Network for Joint Entity and Relation Extraction
Zhiheng Yan, Chong Zhang, Jinlan Fu +2
In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features e…
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…
SpanNER: Named Entity Re-/Recognition as Span Prediction
Jinlan Fu, Xuanjing Huang, Pengfei Liu
Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span predi…
Larger-Context Tagging: When and Why Does It Work?
Jinlan Fu, Liangjing Feng, Qi Zhang +2
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a r…
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
Sebastian Ruder, Noah Constant, Jan Botha +8
Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the sta…
ExplainaBoard: An Explainable Leaderboard for NLP
Pengfei Liu, Jinlan Fu, Yang Xiao +7
With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal…
Towards More Fine-grained and Reliable NLP Performance Prediction
Zihuiwen Ye, Pengfei Liu, Jinlan Fu +1
Performance prediction, the task of estimating a system's performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explos…
RethinkCWS: Is Chinese Word Segmentation a Solved Task?
Jinlan Fu, Pengfei Liu, Qi Zhang +1
The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of…
Interpretable Multi-dataset Evaluation for Named Entity Recognition
Jinlan Fu, Pengfei Liu, Graham Neubig
With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at…
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
Jinlan Fu, Pengfei Liu, Qi Zhang +1
While neural network-based models have achieved impressive performance on a large body of NLP tasks, the generalization behavior of different models remains poorly understood: Does…
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
Minlong Peng, Xiaoyu Xing, Qi Zhang +2
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive…
Learning Task-specific Representation for Novel Words in Sequence Labeling
Minlong Peng, Qi Zhang, Xiaoyu Xing +3
Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor…