Papers by Qiwei Peng
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
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| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
| Outcome: | Empirical results show that paraphrase models and different sentence encoders appear successful on evaluations, but measuring the degree of semantic overlap remains a big challenge for them. |
Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed. |
| Approach: | They propose a framework to evaluate Chinese language teachers' pedagogical competence against international standards. |
| Outcome: | The proposed framework evaluates 13 latest multilingual and Chinese LLMs against international standards for Chinese language teachers. |
Debiasing Multilingual LLMs in Cross-lingual Latent Space (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
Predicate-Argument Based Bi-Encoder for Paraphrase Identification (2022.acl-long)
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| Challenge: | Cross-encoders have been widely applied to sentence pair tasks . bi-encodings have significantly lower computation complexity and are better suited to symmetric tasks. |
| Approach: | They propose a bi-encoder approach to the paraphrase identification task and incorporate predicate-argument information into SBERT. |
| Outcome: | The proposed model outperforms SBERT/SRoBERTa on six paraphrase identification datasets with a minimal increase in parameters. |
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders (2022.coling-1)
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
| Outcome: | The proposed approach improves performance and interpretability for various sentence encoders. |
Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection (2026.eacl-long)
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Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kopčan, Qiwei Peng, Tomáš Javůrek, Michal Gregor, Marian Simko
| Challenge: | Recent advances in multilingual Large Language Models have enabled powerful capabilities for cross-lingual fact-checking. |
| Approach: | They evaluate six open-source multilingual LLMs across 20 languages using a fully multilingual prompting strategy. |
| Outcome: | The proposed model performs better on high-resource languages than on low-resourced ones. |
Tokenization Falling Short: On Subword Robustness in Large Language Models (2024.findings-emnlp)
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| Challenge: | Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary. |
| Approach: | They propose to tokenize raw text into sequences of subword identifiers from a predefined vocabulary . they also investigate the challenges and their impact on large language models . |
| Outcome: | The proposed model can mitigate tokenization issues, but still suffer from typos and other variations. |
Understanding Subword Compositionality of Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) take sequences of subwords as input, requiring them to compose subword representations into meaningful word-level representations. |
| Approach: | They propose to probe how large language models compose subword information . they find structural similarity, semantic decomposability, and form retention are key aspects . |
| Outcome: | The proposed models can be classified into three distinct groups, the authors show . they show that they can achieve great performance when probing layer by layer their sensitivity to semantic decompositionality . |
On Training Data Influence of GPT Models (2024.emnlp-main)
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| Challenge: | generative language models have redefined performance standards across tasks . current research on the influence of training data on autoregressivity remains underexplored . |
| Approach: | They propose a parameterized simulation to assess the impact of training examples on the training dynamics of GPT models. |
| Outcome: | The proposed approach compares existing methods with existing methods across training scenarios in generative language models, spanning tasks across 14 million to 2.8 billion parameters. |
Concept Space Alignment in Multilingual LLMs (2024.emnlp-main)
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| Challenge: | Multilingual large language models generalize somewhat across languages, but it is unclear whether this is a result of improved, implicit alignment, or of something else, e.g., linguistic overlap or semi-parallel subsets of training data. |
| Approach: | They hypothesize that implicit alignment is the reason for generalization in multilingual large language models. |
| Outcome: | The proposed model generalizes well across languages, but lacks linearity. |
Representing Syntax and Composition with Geometric Transformations (2021.findings-acl)
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| Challenge: | Existing models of word meaning are based on syntactic rather than proximal co-occurrences, but they are not suitable for syntax sensitive composition. |
| Approach: | They propose to encode syntactic structure by extending the Skip-Gram with Negative sampling architecture from word2vec. |
| Outcome: | The proposed models perform favourably on benchmark word similarity tasks on similarity tests on similar words compared to models based on proximal co-occurrence . however, the real promise of distributional models is the potential for syntax-sensitive composition. |
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)
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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott
| Challenge: | FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. |
| Approach: | They evaluate vision–language Models and large language models on unseen food images and corresponding questions. |
| Outcome: | The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions. |
o-MEGA: Optimized Methods for Explanation Generation and Analysis (2025.emnlp-demos)
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| Challenge: | a growing number of transformer-based language models have created challenges for model transparency and trustworthiness. |
| Approach: | They propose a tool to automatically identify the most effective explainable AI methods . they evaluate o-mega on a post-claim matching pipeline using a curated dataset . |
| Outcome: | The proposed tool shows that the most effective explainable AI methods can be implemented in semantic matching tasks. |
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization (2024.lrec-main)
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| Challenge: | Existing benchmarks focus on translating English prompts to multilingual codes or have been constrained to very limited natural languages (NLs). |
| Approach: | They propose a benchmark to evaluate multilingual LLMs using multiple natural languages. |
| Outcome: | The proposed benchmarks focus on translating English prompts to multilingual code or have been constrained to very limited natural languages (NLs). |