Papers by Felix Yu
InFillmore: Frame-Guided Language Generation with Bidirectional Context (2021.starsem-1)
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| Challenge: | Existing methods for automatic story plan generation use coarse-to-fine representations of semantic content. |
| Approach: | They propose a structured extension to bidirectional-context conditional language generation, or "infilling" they propose evocative frame annotations and a method for frame-guided generation that leverages frame semantic lexical units. |
| Outcome: | The proposed method allows for explicit manipulation of intended infill semantics with minimal loss of distinguishability from human-generated text. |
Automatic Engineering of Long Prompts (2024.findings-acl)
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| Challenge: | Recent research has explored automatic prompt engineering for short prompts, typically consisting of one or a few sentences. |
| Approach: | They propose an algorithm that automatically improves long prompts by combining a greedy algorithm with beam-search to enhance the effectiveness of LLM-based mutation. |
| Outcome: | The proposed algorithm achieves 9.2% accuracy gain on eight tasks in Big Bench Hard and consistent improvements on GSM8K with various models. |
DP-NMT: Scalable Differentially Private Machine Translation (2024.eacl-demo)
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| Challenge: | Neural machine translation (NMT) is a popular text generation task, yet there is nagging data privacy concerns. |
| Approach: | They propose an open-source framework for a privacy-preserving NMT with DP-SGD. |
| Outcome: | The proposed framework is open-source and open to the public . it combines models, datasets, and evaluation metrics to demonstrate its effectiveness. |
Regression Aware Inference with LLMs (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. |
| Approach: | They propose alternative inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. |
| Outcome: | The proposed approach significantly improves over baselines across datasets and models. |
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. |
Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |
Semantic Label Smoothing for Sequence to Sequence Problems (2020.emnlp-main)
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Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu, Sanjiv Kumar
| Challenge: | Existing methods for seq2seq regularization use label smoothing, but it is difficult to extend it to other datasets. |
| Approach: | They propose a method that smooths over well formed relevant sequences that are semantically similar to the target sequence. |
| Outcome: | The proposed method shows a consistent and significant improvement over the state-of-the-art methods on different datasets. |