Papers by Ruohao Guo
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation (2025.emnlp-main)
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| Challenge: | Current studies evaluate LLMs on explicit false statements, overlooking how misinformation manifests subtly as unchallenged premises in real-world interactions. |
| Approach: | They propose to use EchoMist to analyze implicit misinformation from diverse sources . they also investigate two mitigation methods to enhance LLMs’ capability to counter implicit mis information. |
| Outcome: | The proposed model fails to detect false premises and generate counterfactual explanations. |
Improved Instruction Ordering in Recipe-Grounded Conversation (2023.acl-long)
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| Challenge: | In this paper, we explore the task of instructional dialogue and focus on the cooking domain. |
| Approach: | They propose to explore two auxiliary subtasks to support response generation with improved instruction grounding by incorporating user intent and instruction state information into the model. |
| Outcome: | The proposed model lacks understanding of user intent and inability to track instruction state (i.e., which step was last instructed) incorporating user intent information helps the response generation model mitigate the incorrect order issue. |
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding (2024.acl-long)
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| Challenge: | Existing large language models struggle to capture some language styles without fine-tuning. |
| Approach: | They propose to meta-trained LLMs based on representative lexicons to recognize new styles they have not been fine-tuned on. |
| Outcome: | The proposed method improves zero-shot transfer across styles on 13 established and 63 novel tasks generated with LLMs. |
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) in lowresource languages has been a challenge for years . Existing methods suffer from low quality of annotated data in target language . |
| Approach: | They propose a method that uses projected annotations to generate pseudo supervised data with a transformer language model and a constrained beam search. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance in low-resource languages. |