Papers by Georg Groh
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)
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| Challenge: | Existing methods for topic modeling fail to scale to large datasets or assume one topic per document. |
| Approach: | They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese . |
| Outcome: | The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets. |
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks (2022.acl-long)
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| Challenge: | Existing methods to detect adversarial text inputs are limited in performance and are not detectable via spell checkers. |
| Approach: | They propose a model-agnostic detector of adversarial text examples that detects patterns in the logits of the target classifier when perturbing the input text. |
| Outcome: | The proposed detector improves the state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. |
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (2020.lrec-1)
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Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
| Challenge: | Fine-tuning suffers from catastrophic forgetting, a problem exacerbated in natural language processing (NLP). |
| Approach: | They propose to use progressive neural networks to re-use previously learned knowledge when learning new tasks. |
| Outcome: | The proposed approach improves on common NLP tasks across a range of architectures, datasets, and tasks. |
Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings (2020.lrec-1)
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| Challenge: | Recent generative language models have shown promise in abstractive summarization tasks. |
| Approach: | They propose to use Fr echet embedding distance and angular embeddable similarity to evaluate the performance of generative language models in abstractive summarization tasks. |
| Outcome: | The proposed metric shows close relation with human judgments and has overall better correlations with them. |
Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
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| Challenge: | despite the large amount of labeled datasets, there is an imbalance in data availability across languages. |
| Approach: | They explore cross-lingual knowledge transfer methods avoiding manual data curation . they use large multilingual encoders and translation systems, LLMs, and language adapters . |
| Outcome: | The proposed approaches are tested on three text classification tasks in Ukrainian . the authors show that the proposed approaches avoid manual data curation . |
DIALECTIC: A Multi-Agent System for Startup Evaluation (2026.eacl-industry)
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| Challenge: | Venture capital (VC) investors face a large number of investment opportunities but only invest in few of them. |
| Approach: | They propose an LLM-based system that gathers factual knowledge about a startup and organizes it into a question tree. |
| Outcome: | The proposed system matches the precision of human VCs in predicting startup success. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training (2023.findings-acl)
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| Challenge: | Existing methods to train automatic text simplification systems for languages other than English are limited by the lack of parallel data. |
| Approach: | They propose to use German Easy Language as a corpus of automatic text simplification systems to fine-tune language models to the style characteristics of the language. |
| Outcome: | The proposed language models adapt to the style characteristics of Easy Language and output more accessible texts. |
Adaptive Parameter Compression for Language Models (2025.findings-naacl)
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| Challenge: | Adaptive parameter compression is a new approach to improve NLP models . the current algorithm is based on a single parameter, but it is not scalable. |
| Approach: | They propose a hardware-independent compression strategy that extends the weight-squeezing approach by introducing compression biases and weights. |
| Outcome: | The proposed compression strategy outperforms DistilBERT base models while being significantly more efficient. |