Ning Ding

An Event Ontology Description Framework Based on SKOS

2015

Event ontology is a new paradigm for describing event-based knowledge in web, including action, time, place and objects in event. But it lacks a unified language for description of event classes or event individuals. Simple …

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

2022 · arXiv (Cornell University)

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due …

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

2023 · arXiv (Cornell University)

Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading …

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

2023

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However, when tuning publicly accessible, centralized LLMs with private instruction data, …

HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark?

2025 · arXiv (Cornell University)

Recently, the physical capabilities of (M)LLMs have garnered increasing attention. However, existing benchmarks for physics suffer from two major gaps: they neither provide systematic and up-to-date coverage of real-world physics competitions such as physics Olympiads, …

PTR: Prompt Tuning with Rules for Text Classification

2022 · AI Open

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some few-class classification tasks, such as …

Parameter-efficient fine-tuning of large-scale pre-trained language models

2023 · Nature Machine Intelligence

Abstract With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all …