no code implementations • COLING 2022 • Haochen Li, Tong Mo, Hongcheng Fan, Jingkun Wang, Jiaxi Wang, Fuhao Zhang, Weiping Li
Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.
no code implementations • 19 Mar 2024 • Haochen Li, Di Geng
Therefore, the researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously.
no code implementations • 19 Mar 2024 • Haochen Li, Di Geng
First, we enrich the event embeddings with event argument and structure features by using static AMR graphs and IE graphs; Then, to jointly extract multiple event relations, we use Node Transformer and construct Task-specific Dynamic Event Graphs for each type of relation.
no code implementations • 25 Jan 2024 • Haochen Li, Jonathan Leung, Zhiqi Shen
Large Language Models (LLMs) have shown prominent performance in various downstream tasks in which prompt engineering plays a pivotal role in optimizing LLMs' performance.
no code implementations • 9 Jan 2024 • Haochen Li, Xin Zhou, Zhiqi Shen
In code search, the Generation-Augmented Retrieval (GAR) framework, which generates exemplar code snippets to augment queries, has emerged as a promising strategy to address the principal challenge of modality misalignment between code snippets and natural language queries, particularly with the demonstrated code generation capabilities of Large Language Models (LLMs).
1 code implementation • 12 Oct 2023 • Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE.
no code implementations • 28 Aug 2023 • Haochen Li, Yi Cao, Maria Polukarov, Carmine Ventre
In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data.
no code implementations • 16 Aug 2023 • Haochen Li, Maria Polukarova, Carmine Ventre
We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets.
no code implementations • 6 Jun 2023 • Haochen Li, Tianhao Gao, Jingkun Wang, Weiping Li
Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles.
1 code implementation • 21 Oct 2022 • Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang
In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representation-level augmentation that unifies existing methods.
no code implementations • 3 Oct 2018 • Feng Shi, Haochen Li, Yuhe Gao, Benjamin Kuschner, Song-Chun Zhu
The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators.