Papers by Chenxi Whitehouse
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering (2023.findings-eacl)
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| Challenge: | Current explanation generation models are trained to select the best answers from Multiple-Choice questions or to classify single-word answers to a predetermined vocabulary. |
| Approach: | They propose a multitask learning approach towards a Unified Model for Answer and Explanation generation (UMAE) UMAE models surpass the prior state-of-the-art answer accuracy on A-OKVQA by 10 15%, show competitive results on OK-VQA and VCR, and demonstrate promising out-of domain performance on VQA-X. |
| Outcome: | The proposed model outperforms the state-of-the-art model on A-OKVQA and VCR and shows promising out-of domain performance on VQA-X. |
WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
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| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
| Outcome: | The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web. |
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (2024.eacl-long)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate fluent responses to user queries, but they are also susceptible to misuse in journalism, education, and academia. |
| Approach: | They propose a large-scale benchmark for machine-generated text detection that is a multi-generator, multi-domain, and multi-lingual corpus. |
| Outcome: | The proposed system can detect machine-generated text and pinpoint misuse . the proposed system is based on a large-scale benchmark dataset . |
Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation (2026.acl-industry)
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Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, Chenxi Whitehouse, Adina Williams, Nicola Cancedda
| Challenge: | Existing methods for obtaining well-calibrated uncertainty estimates are poorly calibrated or computationally expensive. |
| Approach: | They propose a linear probe that provides calibrated uncertainty estimates from reasoning judges’ hidden states, requiring no additional model training. |
| Outcome: | The proposed method achieves superior calibration compared to existing methods with x computational savings, generalizes robustly to unseen evaluation domains, and delivers higher accuracy on high-confidence predictions. |
Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling (2026.acl-long)
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Alaa Elsetohy, Sama Hadhoud, Haryo Akbarianto Wibowo, Chenxi Whitehouse, Genta Indra Winata, Fajri Koto, Alham Fikri Aji
| Challenge: | Existing benchmarks test reasoning over culturally grounded premises, but translation-parallel benchmarks inherit English-centric scenarios. |
| Approach: | They propose a template-first benchmark that factorizes reasoning type and cultural aspect across question languages. |
| Outcome: | The proposed benchmark factorizes reasoning type and cultural aspect across question languages. |
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models (2025.acl-long)
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| Challenge: | Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. |
| Approach: | They propose a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance. |
| Outcome: | The proposed framework improves diffusion-based text generation and improves scalability and fluency. |
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching (2022.findings-emnlp)
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| Challenge: | Existing methods for CS use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. |
| Approach: | They propose a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. |
| Outcome: | The proposed method captures fine-grained cross-lingual semantics without corrupting syntax. |
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study (2024.findings-naacl)
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| Challenge: | Pre-trained Large Language Models have significantly advanced NLP, but their ever-increasing size poses significant challenges for conventional fine-tuning. |
| Approach: | They investigate the potential of Low-Rank Adaptation (LoRA) in multilingual summarization, a task that is challenging and relatively unexplored. |
| Outcome: | The proposed method outperforms full fine-tuning and cross-lingual transfer strategies in multilingual summarization tasks. |
What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations (2025.acl-long)
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Dongqi Liu, Chenxi Whitehouse, Xi Yu, Louis Mahon, Rohit Saxena, Zheng Zhao, Yifu Qiu, Mirella Lapata, Vera Demberg
| Challenge: | VISTA dataset contains 18,599 recorded AI conference presentations . large multimodal models exhibit reduced performance in scientific contexts, study shows . |
| Approach: | They propose a dataset specifically designed for video-to-text summarization in scientific domains. |
| Outcome: | This paper compares the performance of large models with human models and shows that they improve on human models. |
LLM-powered Data Augmentation for Enhanced Cross-lingual Performance (2023.emnlp-main)
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| Challenge: | Existing training data for multilingual commonsense reasoning datasets is limited. |
| Approach: | They propose to use large language models for data augmentation in multilingual datasets . they use Dolly-v2, StableVicuna, ChatGPT, and GPT-4 to augment three datasets. |
| Outcome: | The proposed model outperforms larger general-purpose, zero-shot models when training in smaller models. |
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)
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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
| Challenge: | Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA . |
| Approach: | They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities. |
| Outcome: | a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region . |