Papers by Alex Nguyen
ELITR Multilingual Live Subtitling: Demo and Strategy (2021.eacl-demos)
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Ondřej Bojar, Dominik Macháček, Sangeet Sagar, Otakar Smrž, Jonáš Kratochvíl, Peter Polák, Ebrahim Ansari, Mohammad Mahmoudi, Rishu Kumar, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai-Son Nguyen, Felix Schneider, Sebastian Stüker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams
| Challenge: | Using a prototype, we present an automatic speech translation system for live subtitling of conference speech . the system is routinely tested in recognizing English, Czech, and German speech - and presenting it simultaneously into 42 target languages. |
| Approach: | They propose an automatic speech translation system aimed at live subtitling of conference presentations. |
| Outcome: | The proposed system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages. |
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models (2024.findings-acl)
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| Challenge: | Instruction tuning language models can be expensive and expensive to train . current methods require extensive training on large datasets, resulting in high training costs. |
| Approach: | They propose a novel approach to selecting training data based on the learning percentage of the samples. |
| Outcome: | The proposed model performs better on models ranging from 1B to 13B in size compared to training on the entire dataset. |
Towards Robust Mathematical Reasoning (2025.emnlp-main)
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Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu Hoang Trinh, Quoc V Le, Junehyuk Jung
| Challenge: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the international mathematical Olympiad level. |
| Approach: | They propose IMO-Bench, a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
| Outcome: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)
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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock
| Challenge: | Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data . |
| Approach: | They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science . |
| Outcome: | The proposed pipeline improves performance on two large-scale scientific fact-checking datasets. |
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering (2024.naacl-demo)
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| Challenge: | DOCMASTER is a platform for annotating PDF documents, model training, and inference, tailored to document question-answering. |
| Approach: | They propose to integrate layout information into a unified platform for annotating PDF documents, model training, and inference tailored to document question-answering. |
| Outcome: | The proposed platform is designed for annotating PDF documents, model training, and inference, tailored to document question-answering. |
Can Large Language Models Learn Independent Causal Mechanisms? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform poorly on complex reasoning tasks, such as abstract, causal, or logical reasoning. |
| Approach: | They propose to use two concepts from causality to learn ICMs within LLMs to improve out-of-distribution performance on abstract and causal reasoning tasks. |
| Outcome: | The proposed model outperforms existing models on abstract and causal reasoning tasks and is more robust to fine-tuning. |