Mathieu Ravaut

Towards Summary Candidates Fusion

2022 · arXiv (Cornell University)

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap …

A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

2023 · arXiv (Cornell University)

Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for …

PromptSum: Parameter-Efficient Controllable Abstractive Summarization

2023 · arXiv (Cornell University)

Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios. However, …

ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?

2023 · arXiv (Cornell University)

Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce. Through instruction-tuning a large language model (LLM) with supervised fine-tuning and reinforcement …