Papers by Renze Lou
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. |
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks. |
| Approach: | They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks. |
| Outcome: | The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. |
Toward Zero-Shot Instruction Following (2024.eacl-srw)
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| Challenge: | a novel approach to zero-shot cross-task generalization is proposed . prior work relied on demonstrations, but this approach could be overestimated . |
| Approach: | They propose a "demonstration-driven instruction following" setting for zero-shot cross-task generalization . they propose to automatically find out critical sentences in a paragraph-style task definition . |
| Outcome: | The proposed approach yields state-of-the-art performance on the Super-NaturalInstructions. |
Improving English-Arabic Transliteration with Phonemic Memories (2022.findings-emnlp)
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| Challenge: | Existing neural approaches to transliterate names from English to Arabic are limited and focus on leveraging the phonemic association between English and Arabic. |
| Approach: | They propose a model for English-Arabic transliteration using a memory module modeling the phonemic association between English and Arabic to guide the transliterations process. |
| Outcome: | The proposed model improves on EANames corpus, which better represents names in the general public than linked Wikipedia entries that are always names of famous people. |
Advancing Language Models through Instruction Tuning: Recent Progress and Challenges (2025.emnlp-tutorials)
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| Challenge: | tutorial addresses three critical questions within the field of instruction tuning: (1) What are the current focal points in instruction tuning research? (2) What are best practices in training an instruction-following model? (3) What new challenges have emerged? |
| Approach: | This tutorial presents a systematic overview of recent advances in instruction tuning. |
| Outcome: | The tutorial covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. |
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks (2021.acl-long)
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| Challenge: | Prior research has found that only a few attention heads are important in each mono-lingual NLP task and pruning the remaining heads leads to comparable or improved performance of the model. |
| Approach: | They examine the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. |
| Outcome: | The proposed model performs better with the remaining heads pruned than with the other models, the authors show . |
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks (2021.emnlp-main)
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| Challenge: | A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. |
| Approach: | They propose an automatic auxiliary task selection method based on gradient calculation in Transformer-based models that improves MT-DNN performance. |
| Outcome: | The proposed method improves MT-DNN performance on 8 natural language understanding (GLUE) tasks, while costing less than AUTOSEM and comparable GPU consumption. |
The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
| Approach: | They propose a framework that subjects models to discriminative self-assessment under diverse contextual pressures to scrutinize subtle behavioral nuances induced by memory modifications. |
| Outcome: | The proposed framework achieves high benchmarks without overwriting internal beliefs, while recursive modifications accumulate representational residues, triggering cognitive instability and permanently diminishing the reversibility of the model’s memory state. |
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
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| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
| Approach: | They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges . |
| Outcome: | The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems. |