Papers by Haotian Ye
Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs (2026.eacl-long)
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| Challenge: | Medical reasoning in large language models is a complex cognitive process through which clinicians interpret patient data and make diagnostic and therapeutic decisions. |
| Approach: | They propose an evaluation framework that disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks. |
| Outcome: | The proposed evaluation framework disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks. |
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
A Crosslingual Investigation of Conceptualization in 1335 Languages (2023.acl-long)
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Yihong Liu, Haotian Ye, Leonie Weissweiler, Philipp Wicke, Renhao Pei, Robert Zangenfeind, Hinrich Schütze
| Challenge: | Conceptualizer is a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. |
| Approach: | They propose a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. |
| Outcome: | The proposed method has good alignment accuracy across all languages and on 32 Swadesh concepts. |
TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)
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| Challenge: | Existing mPLMs that handle non-transliterated data are not sufficient to train crosslingual models. |
| Approach: | They propose a framework to transliterate related languages into a common script by exploiting existing mPLMs and their tokenizer without any training. |
| Outcome: | The proposed framework can create strong baselines for data that is transliterated into a common script by exploiting an existing mPLM and its tokenizer without any training. |
Taxi1500: A Dataset for Multilingual Text Classification in 1500 Languages (2025.naacl-short)
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| Challenge: | a large-scale text classification dataset encompassing 1504 languages is needed to address this gap . low-resource languages are often overlooked due to the scarcity of evaluation datasets. |
| Approach: | They propose to use translations of the Bible to construct a large-scale text classification dataset that covers 1504 languages and annotate them using crowdsourcing. |
| Outcome: | The proposed dataset covers 1504 languages and is available to the public. |
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models (2024.acl-long)
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| Challenge: | The world’s more than 7000 languages are written in at least 293 scripts, which poses a difficulty for multilingual pretrained language models in learning crosslingual knowledge through lexical overlap. |
| Approach: | They propose a framework that optimizes the Transliteration Contrastive Modeling objective to fine-tune an mPLM by contrasting sentences in its training data and transliterations in a unified script. |
| Outcome: | The proposed model outperforms Glot500-m on zero-shot crosslingual transfer tasks while retaining uniformity across scripts. |
LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)
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| Challenge: | Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information. |
| Approach: | They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction. |
| Outcome: | The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks. |
Privacy-Preserving Federated Learning for Hate Speech Detection (2025.naacl-srw)
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| Challenge: | a federated learning system with differential privacy is tailored to low-resource languages . data with fewer than 20 sentences per client struggled due to excessive noise . |
| Approach: | They propose a federated learning system with differential privacy for hate speech detection . they fine-tuned pre-trained language models to find it to be the most effective . |
| Outcome: | The proposed learning system outperforms other models in low-resource languages . balanced datasets and augmenting hateful data with non-hateful examples proved critical . |
DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)
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Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored. |
| Approach: | They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python. |
| Outcome: | The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python. |
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs (2023.findings-emnlp)
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| Challenge: | Existing work on colexification patterns relies on annotated word lists, limiting scalability and usefulness in NLP. |
| Approach: | They propose two methods to train multilingual graphs from colexification patterns using an unannotated parallel corpus. |
| Outcome: | The proposed methods achieve high recall on CLICS and transfer learning in multilingual graphs. |