Papers by Tu Dinh
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
Topic Modeling for Short Texts via Optimal Transport-Based Clustering (2025.findings-acl)
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| Challenge: | Existing approaches to topic modeling are based on probabilistic graphical models or non-negative matrix factorization techniques. |
| Approach: | They propose a method that aligns global clusters with topics to discover topics and learn document representations in topic space. |
| Outcome: | The proposed method outperforms state-of-the-art techniques in short-text topic modeling across commonly used metrics. |
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization (2020.acl-main)
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| Challenge: | Existing knowledge graph embedding methods do not allow for the prediction of new triples, such as for search personalization tasks. |
| Approach: | They propose a relational memory network to encode potential dependencies in relationship triples by a transformer self-attention mechanism. |
| Outcome: | The proposed model obtains state-of-the-art results on SEARCH17, WN11 and FB13 for the search personalization task, and on a convolutional neural network-based decoder. |
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)
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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues
| Challenge: | Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks. |
| Approach: | They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks. |
| Outcome: | The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading. |