Search Results for author:
Found 14 papers, 5 papers with code
KiPT: Knowledge-injected Prompt Tuning for Event Detection
Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.
SPDiffusion: Semantic Protection Diffusion for Multi-concept Text-to-image Generation
We found that attribute confusion occurs when a certain region of the latent features attend to multiple or incorrect prompt tokens.
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1, 300 indigenous languages and a population of 671 million people.
Prompt-based Visual Alignment for Zero-shot Policy Transfer
With explicit constraints of semantic information, PVA can learn unified cross-domain representation under limited access to cross-domain data and achieves great zero-shot generalization ability in unseen domains.
Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction
Therefore, the researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously.
GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings
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.
Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance.
Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search
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).
Rethinking Negative Pairs in Code Search
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.
An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics
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.
Detecting Financial Market Manipulation with Statistical Physics Tools
We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets.
Joint Event Extraction via Structural Semantic Matching
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.
Exploring Representation-Level Augmentation for Code Search
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.
Sparse Winograd Convolutional neural networks on small-scale systolic arrays
The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators.
