Search Results for author:
Found 15 papers, 5 papers with code
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models
To the best of our knowledge, we are the first to build a benchmark dataset and solutions for academic-to-general-audience text paraphrase dataset.
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling
Experiments on news benchmark datasets and one Twitter dataset demonstrate the method's superiority in generating coherent, diverse topics, and handling noisy data, outperforming strong baselines.
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model
Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc.
Are Large Language Models True Healthcare Jacks-of-All-Trades? Benchmarking Across Health Professions Beyond Physician Exams
To fill this research gap, we introduce the Examinations for Medical Personnel in Chinese (EMPEC), a pioneering large-scale healthcare knowledge benchmark in traditional Chinese.
No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer
This helps address the lack of interpretability in existing TKGR models and provides a universal explanation approach applicable across various models.
Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models
In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction.
Zero-shot Temporal Relation Extraction with ChatGPT
The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem.
Clustering-based Unsupervised Generative Relation Extraction
To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information.
Efficient text generation of user-defined topic using generative adversarial networks
The objective results showed that the proposed method is capable of generating texts with less time than others and the generated text is related to the user-defined topic and sentiment.
Personalized sentence generation using generative adversarial networks with author-specific word usage
The author-specific word usage is a vital feature to let readers perceive the writing style of the author.
