Papers by Linyong Wang

2 papers
OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

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Challenge: Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach: They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome: The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)

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Challenge: Existing methods for autoregressive text generation have low controllability and accumulating errors.
Approach: They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy.
Outcome: Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance.

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