Papers by Dominik Stammbach
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. |
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. |
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task (D19-66)
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| Challenge: | Existing methods of fact checking are based on the assignment of a truth value to a given (factual) statement, and therefore it is desirable to have access to the evidence used to reach an assignment. |
| Approach: | They propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. |
| Outcome: | The proposed system beats the top performing systems of the first FEVER challenge which act as a baseline, beating 64.21% of the top-performing systems. |
Environmental Claim Detection (2023.acl-short)
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| Challenge: | a growing number of environmental claims are being made by companies in the face of climate change. |
| Approach: | They propose a task of environmental claim detection to detect environmental claims at scale . they use an expert-annotated dataset and models trained on this dataset to do this . |
| Outcome: | The proposed task detects environmental claims in quarterly earning calls . the number of environmental claims has steadily increased since the Paris Agreement in 2015 . |
Aligning Large Language Models with Diverse Political Viewpoints (2024.emnlp-main)
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| Challenge: | Large language models such as ChatGPT exhibit striking political biases . a recent study shows that chatbots exhibit progressive, liberal, and proenvironmental biase . |
| Approach: | They propose to align large language models with 100,000 comments from candidates running for national parliament in Switzerland. |
| Outcome: | The proposed model generates more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. |
The Law and NLP: Bridging Disciplinary Disconnects (2023.findings-emnlp)
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| Challenge: | Legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP) the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. |
| Approach: | They argue that legal practitioners are slow to adopt natural language processing (NLP) they argue that there is a disconnect between legal needs and NLP research . |
| Outcome: | The proposed tasks bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent (2024.acl-long)
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| Challenge: | Legal passage retrieval is a practice-oriented task that seeks to predict relevant passages from precedential court decisions given the context of a legal argument. |
| Approach: | They present a dataset which aims to facilitate work on legal passage retrieval . they extensively evaluate various approaches and find classification-based retrieval works best . |
| Outcome: | The proposed dataset aims to facilitate work on legal passage retrieval . it shows that classification-based retrieval seems to work best . |
Revisiting Automated Topic Model Evaluation with Large Language Models (2023.emnlp-main)
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| Challenge: | Topic models are an unsupervised dimensionality reduction technique that help organize large text collections. |
| Approach: | They propose to use large language models to evaluate document output and determine optimal number of topics. |
| Outcome: | The proposed model performs better on coherence ratings of word sets than on intrustion detection. |