Papers by Ruifeng Luo
Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Empirical results show that pre-trained language models can improve the typical answer generation of GLMs. |
| Approach: | They propose a ranking strategy that exploits WordNet to train a ranker that picks out the most popular answers for commonsense questions. |
| Outcome: | The proposed ranking strategy is tested on a commonsense question answering (QA) dataset and on negative samples from WordNet. |
AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on cognitive abilities, such as knowledge retrieval, complex reasoning, and instruction following, largely overlooking empathy evaluation. |
| Approach: | They propose to benchmark two core empathetic capabilities of omnimodal large models (OLMs) generating empatries by comprehending affective cues from multi-modal inputs and judging empathy of audio responses without relying on text transcription. |
| Outcome: | The proposed benchmark outperforms existing models with audio output capabilities but is unreliable for evaluating fine-grained paralinguistic expressiveness. |
Large Language Models as Reader for Bias Detection (2025.findings-emnlp)
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| Challenge: | Traditional methods analyze text from the writer’s perspective, leaving the reader’s viewpoint underexplored. |
| Approach: | They investigate whether large language models can be leveraged as readers for bias detection by generating reader-perspective comments. |
| Outcome: | The proposed model performs comparable to GPT4's in detecting bias in media content. |
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)
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| Challenge: | Existing methods to separate content from style but some words contain both content and style information. |
| Approach: | They propose a method which uses a reversible encoder to improve content disentanglement. |
| Outcome: | The proposed method outperforms baselines on sentiment transfer and formality transfer tasks. |
A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking (2026.findings-eacl)
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| Challenge: | Learning-style queries can reliably elicit harmful responses, highlighting a critical safety blind spot in modern LLMs. |
| Approach: | They propose a new reframing paradigm that hides intention by learning from LLMs and uses 4 conceptual components to construct learning-style queries. |
| Outcome: | The proposed framework achieves top attack success rates on most models and across malicious categories while maintaining high efficiency with concise prompts. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |