Papers by Yao Rong
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)
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| Challenge: | Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data. |
| Approach: | They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data. |
| Outcome: | The proposed framework improves the fidelity of the generated texts to the input structured data. |
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models (2024.findings-acl)
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| Challenge: | Contemporary approaches to generate tabular data are limited due to the lack of external knowledge. |
| Approach: | They propose to use proximal policy optimization to apply GANs and fine-tune Large Language Models to enhance the probability distribution of tabular features. |
| Outcome: | The proposed method improves accuracy of GANs and LLMs over state-of-the-art over three real-world datasets. |
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models (2026.findings-acl)
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Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia
| Challenge: | Currently, evaluation criteria and methods used for jailbreak effectiveness are inconsistent. |
| Approach: | They propose a framework to measure jailbreak effectiveness using a model that filters out jailbreak noise while preserving the original malicious question. |
| Outcome: | The proposed framework outperforms existing evaluation methods on a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances. |