Search Results for author:
Found 12 papers, 6 papers with code
Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences
In this paper, we introduce the problem of dictionary example sentence generation, aiming to automatically generate dictionary example sentences for targeted words according to the corresponding definitions.
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation.
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems.
Enhancing Few-Shot Stock Trend Prediction with Large Language Models
Inspired by the impressive few-shot capability of Large Language Models (LLMs), we propose using LLMs in a few-shot setting to overcome the scarcity of labeled data and make prediction more feasible to investors.
A Survey of Generative Techniques for Spatial-Temporal Data Mining
By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.
Improving Factual Error Correction by Learning to Inject Factual Errors
Given the lack of paired data (i. e., false claims and their corresponding correct claims), existing methods typically adopt the mask-then-correct paradigm.
Noisy Pair Corrector for Dense Retrieval
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched.
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance.
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
The dual-encoder has become the de facto architecture for dense retrieval.
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning
Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relational reasoning and compositional generalization capabilities.
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
To overcome this challenge, we used a classifier to instruct the MCMC-based models where and how to refine the candidate sentences.
