Papers by Tianyi Luo
Research Replication Prediction Using Weakly Supervised Learning (2020.findings-emnlp)
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| Challenge: | Existing methods to predict scientific claims’ replicability use only hand-extracted statistics features without utilizing research papers’ text information. |
| Approach: | They propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. |
| Outcome: | The proposed methods achieve an accuracy of 75.76% over real-world datasets. |
Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)
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| Challenge: | Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge. |
| Approach: | They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs. |
| Outcome: | The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets. |
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot text classification suffer from overfitting due to the lack of matching between the few amount of samples and complicated models. |
| Approach: | They propose a method to improve model generalization ability to a new task by leveraging a meta-learner via gradient similarity method. |
| Outcome: | The proposed method improves few-shot text classification performance on several benchmarks. |
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)
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| Challenge: | Existing methods for predicting research replication are insufficient especially for long research papers. |
| Approach: | They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets. |
| Outcome: | The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance. |
Quantize What Counts: More for Keys, Less for Values (2026.findings-acl)
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| Challenge: | Empirical evaluations across various prominent LLMs and benchmarks show that key-favored allocations retain up to 98.3% accuracy compared to uniform allocations (e.g., 4-bit keys, 2-bit values). |
| Approach: | They propose two theorems that anchor mixed-precision KV quantization in the intrinsic geometry of Transformer models. |
| Outcome: | Empirical evaluations show that key-favored allocations retain up to 98.3% accuracy while conserving memory. |
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)
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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen
| Challenge: | Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent. |
| Approach: | They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons. |
| Outcome: | The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |