Search Results for author: Yaojie Lu

Found 31 papers, 17 papers with code

Few-shot Named Entity Recognition via Superposition Concept Discrimination

1 code implementation25 Mar 2024 Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.

Active Learning few-shot-ner +4

Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models

1 code implementation14 Mar 2024 Zhuoqun Li, Hongyu Lin, Yaojie Lu, Hao Xiang, Xianpei Han, Le Sun

Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs.

ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

no code implementations6 Mar 2024 Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, WeiPeng Chen

As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters.

Quantization

SoFA: Shielded On-the-fly Alignment via Priority Rule Following

no code implementations27 Feb 2024 Xinyu Lu, Bowen Yu, Yaojie Lu, Hongyu Lin, Haiyang Yu, Le Sun, Xianpei Han, Yongbin Li

The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.

Executing Natural Language-Described Algorithms with Large Language Models: An Investigation

1 code implementation23 Feb 2024 Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.

Natural Language Understanding

Self-Retrieval: Building an Information Retrieval System with One Large Language Model

no code implementations23 Feb 2024 Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li

The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.

Information Retrieval Language Modelling +2

Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?

no code implementations22 Feb 2024 Ning Bian, Xianpei Han, Hongyu Lin, Yaojie Lu, Ben He, Le Sun

Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.

Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting

no code implementations22 Nov 2023 Xinyan Guan, Yanjiang Liu, Hongyu Lin, Yaojie Lu, Ben He, Xianpei Han, Le Sun

Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).

Hallucination Language Modelling +1

Learning In-context Learning for Named Entity Recognition

2 code implementations18 May 2023 Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .

few-shot-ner Few-shot NER +4

Harvesting Event Schemas from Large Language Models

1 code implementation12 May 2023 Jialong Tang, Hongyu Lin, Zhuoqun Li, Yaojie Lu, Xianpei Han, Le Sun

Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge.

Universal Information Extraction as Unified Semantic Matching

no code implementations9 Jan 2023 Jie Lou, Yaojie Lu, Dai Dai, Wei Jia, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.

Procedural Text Understanding via Scene-Wise Evolution

no code implementations15 Mar 2022 Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.

Procedural Text Understanding

ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling

1 code implementation SEMEVAL 2020 Yaojie Lu, Annan Li, Hongyu Lin, Xianpei Han, Le Sun

ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.

counterfactual Question Answering

End-to-End Neural Event Coreference Resolution

1 code implementation17 Sep 2020 Yaojie Lu, Hongyu Lin, Jialong Tang, Xianpei Han, Le Sun

Traditional event coreference systems usually rely on pipeline framework and hand-crafted features, which often face error propagation problem and have poor generalization ability.

coreference-resolution Event Coreference Resolution +1

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?

no code implementations EMNLP 2020 Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan

Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models.

named-entity-recognition Named Entity Recognition +1

Iterative Dual Domain Adaptation for Neural Machine Translation

no code implementations IJCNLP 2019 Jiali Zeng, Yang Liu, Jinsong Su, Yubin Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.

Domain Adaptation Knowledge Distillation +4

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition

no code implementations IJCNLP 2019 Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang

Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck.

named-entity-recognition Named Entity Recognition +1

Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning

1 code implementation ACL 2019 Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words.

Event Detection Representation Learning

Cost-sensitive Regularization for Label Confusion-aware Event Detection

1 code implementation ACL 2019 Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type.

Event Detection Vocal Bursts Type Prediction

Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks

1 code implementation ACL 2019 Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i. e., although a mention can nest other mentions, they will not share the same head word.

NER Nested Mention Recognition +1

Adaptive Scaling for Sparse Detection in Information Extraction

1 code implementation ACL 2018 Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes.

Nugget Proposal Networks for Chinese Event Detection

1 code implementation ACL 2018 Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese.

Event Detection General Classification

Variational Recurrent Neural Machine Translation

no code implementations16 Jan 2018 Jinsong Su, Shan Wu, Deyi Xiong, Yaojie Lu, Xianpei Han, Biao Zhang

Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.

Machine Translation NMT +2

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