Papers by Randy Goebel
Locally Distributed Activation Vectors for Guided Feature Attribution (2022.coling-1)
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| Challenge: | Existing methods to explain predictions of deep neural networks are unstable and do not always provide faithful explanations to the target model. |
| Approach: | They propose a method to learn explanations-specific representations while constructing deep network models for text classification. |
| Outcome: | The proposed method improves model interpretability while preserving predictive performance. |
RANCC: Rationalizing Neural Networks via Concept Clustering (2020.coling-main)
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| Challenge: | Existing models that construct explanations concurrently with classification predictions are opaque. |
| Approach: | They propose a self-explainable model for Natural Language Processing (NLP) text classification tasks . they extract a rationale from the text and use it to predict a concept of interest . |
| Outcome: | The proposed model can be compressed without complicated compression techniques. |
DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector (2021.eacl-main)
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| Challenge: | Neural networks (NNs) are becoming deeper and more complex, making them difficult to understand and interpret. |
| Approach: | They propose a method to distill knowledge concurrently from any neural network architecture for text classification. |
| Outcome: | The proposed method achieves better performance than the target black-box and provides better explanations than existing techniques. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
TRM-Planner: Offline Target Planning and Distillation for Tiny Recursive Models (2026.findings-acl)
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| Challenge: | Tiny Recursive Models (TRMs) perform iterative reasoning with an Adaptive Computation Time (ACT)-style loop, but their supervised training targets can be brittle and their halting behavior difficult to tune. |
| Approach: | They propose a two-stage teacher-cache distillation recipe that shifts compute to offline teacher-caching stage. |
| Outcome: | The proposed model improves su-pervision while leaving student-time inference unchanged. |