Papers by Minghan Wang
Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts (2025.findings-acl)
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Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Yuxia Wang, Mukhammed Togmanov, Jonibek Mansurov, Askhat Sametov, Nurdaulet Mukhituly, Minghan Wang, Daniil Orel, Zain Muhammad Mujahid, Fajri Koto, Timothy Baldwin, Preslav Nakov
| Challenge: | Large language models (LLMs) have the potential to generate harmful content, posing risks to users. |
| Approach: | They propose a dataset specifically designed for safety evaluation in Kazakh and Russian . they use a bilingual context in Kazakhstan where both Kazakh (a low-resource language) and Russian (a high-resourced language) |
| Outcome: | The proposed dataset is designed for safety evaluation in Kazakh and Russian . it shows that both multilingual and language-specific LLMs perform better than others . |
Rethinking STS and NLI in Large Language Models (2024.findings-eacl)
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| Challenge: | Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks. |
| Approach: | They propose to rethink semantic textual similarity (STS) and natural language inference (NLI) models with task-specific prompts and model overconfidence to capture disagreements between human judgements. |
| Outcome: | The proposed models are able to capture human opinions on individual examples without any parameter modifications. |
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs (2025.coling-main)
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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models (LLMs) generate naturallysounding answers over a broad range of human inquiries, but they still produce content that deviates from real-world facts. |
| Approach: | They propose a framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document. |
| Outcome: | The proposed framework assesses the factuality of free-form responses in open domains and evaluates factually of LLMs. |
Discrete Minds in a Continuous World: Do Language Models Know Time Passes? (2025.findings-emnlp)
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| Challenge: | Large Language Models excel at temporal reasoning tasks, but their ability to perceive the passage of time remains unexplored. |
| Approach: | They propose a Token-Time Hypothesis to test whether LLMs perceive the passage of time . they also propose an interactive navigation challenge to examine how LLM responds to time pressure . |
| Outcome: | The proposed model can map discrete token counts to wall-clock time and validate this through a dialogue duration judgment task. |
Loki: An Open-Source Tool for Fact Verification (2025.coling-demos)
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Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing, Yilin Geng, Zenan Zhai, Preslav Nakov, Timothy Baldwin
| Challenge: | Loki is an open-source fact-checking tool designed to address the growing problem of misinformation. |
| Approach: | They propose a tool that breaks down the fact-checking task into five steps . they propose LOKI, which offers a semiautomated, human-in-the-loop approach . |
| Outcome: | a new open-source tool is designed to address the growing problem of misinformation . the tool breaks down the fact-checking task into five steps to assist human judgment . |
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)
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| Challenge: | Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions. |
| Approach: | They propose to capture the human disagreement distribution from the perspective of model calibration. |
| Outcome: | The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy. |
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs (2024.findings-emnlp)
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| Challenge: | Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. |
| Approach: | They propose to use contrastive learning with masked LMs and supervised fine-tuning with large language models to improve empathy understanding in NLP models. |
| Outcome: | The proposed methods show that there is low agreement among annotators and that cultural differences are a factor in their interpretation of empathy. |
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)
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| Challenge: | Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module. |
| Approach: | They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information. |
| Outcome: | The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets. |
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) often produce content that deviates from real-world facts. |
| Approach: | They developed a unified framework to assess the factuality of large language models . open-sourced framework is publicly available as a Python library and web service . |
| Outcome: | OpenFactCheck is open-sourced and publicly released as a Python library and web service. |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
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| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model (2025.findings-acl)
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| Challenge: | Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs. |
| Approach: | They propose to use large language models to translate culturally rooted proverbs in conversation and between languages with similar cultural backgrounds to compare their results. |
| Outcome: | The proposed models can achieve good translation between languages with similar cultural backgrounds and outperform NMT models in proverb translation. |
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging. |
| Approach: | They propose a task that focuses on transcribing scientific conference videos by leveraging visual information from slides to enhance the accuracy of technical terminologies. |
| Outcome: | The proposed framework improves transcript quality through post-editing and improves performance over speech-only baselines. |
Factuality of Large Language Models: A Survey (2024.emnlp-main)
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Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Das, Preslav Nakov
| Challenge: | Large language models (LLMs) are factually incorrect, which limits their applicability in real-world scenarios. |
| Approach: | They analyze existing work to identify major challenges and their associated causes . they propose to evaluate LLMs using a variety of measures to mitigate factual errors . |
| Outcome: | The proposed methods are based on a variety of datasets and proposed strategies to mitigate factual errors. |
Towards Inference-time Scaling for Continuous Space Reasoning (2026.findings-acl)
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| Challenge: | Recent advances in reasoning large language models have expanded along training and inferencetime dimensions. |
| Approach: | They propose to use COCONUT (CITATION) continuous space reasoning LM as the backbone to generate diverse reasoning paths through dropout-based sampling. |
| Outcome: | The proposed method could enable performance gains similar to those observed in the discrete space, but only marginally improves in the continuous space. |