Xiaodan Liang

Data-to-Text Generation with Style Imitation

2019 · arXiv (Cornell University)

Recent neural approaches to data-to-text generation have mostly focused on improving content fidelity while lacking explicit control over writing styles (e.g., word choices, sentence structures). More traditional systems use templates to determine the realization of …

EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation

2021 · arXiv (Cornell University)

Pre-trained language models have shown remarkable results on various NLP tasks. Nevertheless, due to their bulky size and slow inference speed, it is hard to deploy them on edge devices. In this paper, we have …

ATG: Benchmarking Automated Theorem Generation for Generative Language Models

2024

Humans can develop new theorems to explore broader and more complex mathematical results.While current generative language models (LMs) have achieved significant improvement in automatically proving theorems, their ability to generate new or reusable theorems is …

UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation

2024 · arXiv (Cornell University)

We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. This span uncertainty enhances model calibration, improving robustness and mitigating semantic …

BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous …