Papers by Jingwei Ni
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools (2023.emnlp-demo)
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Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
| Challenge: | a lack of transparency in sustainability reporting is a key challenge due to the sheer volume and complexity of sustainability reports . only a few entities worldwide have the resources to analyze these reports at scale . a novel LLM-based system to automate the analysis of corporate sustainability reports is needed . |
| Approach: | They propose a novel LLM-based system to automate the analysis of corporate sustainability reports. |
| Outcome: | The proposed system automates the analysis of corporate sustainability reports. |
Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation (2026.acl-long)
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| Challenge: | Existing systems that detect logical fallacies in public discourse do not help people recognize them independently. |
| Approach: | They propose an intelligent tutoring system which uses large language models to help humans learn about logical fallacies. |
| Outcome: | The proposed system outperforms baseline LLMs lacking such pedagogical strategies. |
ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures (2024.emnlp-main)
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| Challenge: | Qualitative disclosures typically include narrative descriptions of climate-related risks, opportunities, strategies, and governance. |
| Approach: | They simulate typical tasks of a sustainability analyst by examining 30 sustainability reports with 16 detailed climate-related questions. |
| Outcome: | The proposed model combines expert knowledge with embeddings in a dataset with over 8.5K unique question-source-answer pairs labeled by different levels of relevance. |
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026.acl-demo)
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Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, Minh Ngoc Ta, Ekaterina Fadeeva, Artem Vazhentsev, Daria Galimzianova, Nikolai Rozanov, Viktor Mazanov, Jingwei Ni, Tianyi Wu, Igor Kiselev, Mrinmaya Sachan, Iryna Gurevych, Preslav Nakov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Existing TTC scaling strategies and reasoning scorers are fragmented and evaluated under inconsistent protocols. |
| Approach: | They propose a framework for seamless test-time compute scaling of large language model reasoning . they use a modular Python library to implement state-of-the-art scaling strategy and scorer families . |
| Outcome: | The proposed framework evaluates performance and computational efficiency on mathematical and coding tasks. |
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators (2024.acl-long)
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| Challenge: | generative AI is a counter-measure to misinformation, but factual claim detection suffers from inconsistency in definitions and high cost of manual annotation. |
| Approach: | They propose a framework that assists in the annotation of factual claims with the help of large language models. |
| Outcome: | The proposed framework can be used to annotate factual claims with the help of large language models and can work with or without expert supervision. |
DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | RAG systems leave out important relevant information (low recall) and excessively related but irrelevant information (high precision) authors propose a manual annotation-free schema that can be used for RAGs with limited performance. |
| Approach: | They propose a manual annotation-free schema that annotates unseen queries with calibrated relevance scores. |
| Outcome: | Evaluators show that DIRAS can achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs. |
Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? (2026.eacl-long)
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Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Alexander Miserlis Hoyle, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Elliott Ash
| Challenge: | Variation in human annotation (i.e., disagreements) is common in NLP, but it is unclear whether it is possible to model this variation in LLMs. |
| Approach: | They evaluate the influence of different reasoning settings on LLM disagreement modeling . RLVR-style reasoning degrades performance in disagreement modeling, they find . |
| Outcome: | The proposed reasoning settings improve LLM disagreement modeling, while RLVR-style reasoning degrades it. |
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP (2021.emnlp-main)
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Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schoelkopf
| Challenge: | a meta-analysis of published studies shows that the causal direction of data collection can explain some trends in NLP . semi-supervised learning and domain adaptation performance differ on a number of tasks . |
| Approach: | They argue that the causal direction of the data collection process has nontrivial implications . authors categorize common NLP tasks according to their causal direction . they also empirically assay the validity of the ICM principle for text data . |
| Outcome: | The proposed model can explain differences in semi-supervised learning and domain adaptation performance across settings. |
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)
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| Challenge: | Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them. |
| Approach: | They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets. |
| Outcome: | The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it. |
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification (2025.emnlp-demos)
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Chenfei Xiong, Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash
| Challenge: | Social scientists often need to develop codebooks that can be reliable but require significant human effort. |
| Approach: | They propose a mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models. |
| Outcome: | The proposed framework integrates human expertise with automatic annotation guided by large language models. |
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering (2024.acl-long)
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| Challenge: | Evidence-Based QA has proved insufficiently faithful with Large Language Models . a typical application of LLMs is in Evidence-based Question Answering (QA). |
| Approach: | They propose a data generation pipeline with automated data quality filters to fine-tune LLMs for better source quality and answer attributability. |
| Outcome: | The proposed model can synthesize high-quality training and testing data at scale. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models (2026.acl-long)
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Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, Mubashara Akhtar, Jiaheng Zhang, Elliott Ash, Markus Leippold, Timothy Baldwin, See-Kiong Ng, Artem Shelmanov, Mrinmaya Sachan
| Challenge: | Existing verification approaches, such as Process Reward Models, are computationally expensive and limited to specific domains. |
| Approach: | They propose a transformer-based probe that uses internal states of frozen LLMs to estimate credibility of reasoning steps during generation. |
| Outcome: | The proposed probes match or exceed PRMs that are up to 810 larger. |
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance (2022.naacl-main)
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| Challenge: | Existing work on translationese neglects important factors and conclusions are mostly correlational but not causal. |
| Approach: | They use a dataset where MT training data are also labeled with human translation directions to examine the impact of translationese on machine translation evaluation. |
| Outcome: | The proposed model learns in the same direction as human translation directions. |