Papers by Letitia Parcalabescu
Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation (2026.eacl-long)
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Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, Michael Barlow, Gregor Ziegltrum, Volker Stampa, Bastian Harren, Björn Deiseroth
| Challenge: | Recent studies show that data quality can significantly boost performance and training efficiency for large language models. |
| Approach: | They propose a German-language dataset curation pipeline that combines heuristic and model-based filtering techniques with synthetic data generation. |
| Outcome: | The proposed pipeline can be used to create a large-scale German pre-training dataset using common Crawl web data, fineweb2 and synthetically generated data conditioned on real, organic web data. |
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks (2023.acl-long)
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| Challenge: | Vision and language models exploit unrobust indicators in individual modalities instead of focusing on relevant information in each modality. |
| Approach: | They propose a performance-agnostic multimodality score based on Shapley values that quantifies in which proportions a multimodal model uses individual modalities. |
| Outcome: | The proposed model can quantify in which proportions a multimodal model uses individual modalities for different tasks and datasets. |
On Measuring Faithfulness or Self-consistency of Natural Language Explanations (2024.acl-long)
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| Challenge: | Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought explanations, but they can also make unfaithful explanations that hide their sensitivity to biasing inputs. |
| Approach: | They propose to use a model-based consistency test to judge the faithfulness of post-hoc or Chain-of-Thought explanations rather than model-specific faithfulness tests. |
| Outcome: | The proposed tests do not measure faithfulness to model explainability but rather their self-consistency at output level. |
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena (2022.acl-long)
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| Challenge: | Current models have difficulty addressing most phenomena, despite performance gains on many V&L tasks. |
| Approach: | They propose a benchmark for testing general-purpose pretrained vision and language models for their visio-linguistic grounding capabilities on specific linguistic phenomena. |
| Outcome: | The proposed model can be used to evaluate models on linguistic phenomena in the visual modality. |
AMR Similarity Metrics from Principles (2020.tacl-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are rooted, acyclic, directed, and edge-labeled. |
| Approach: | They propose a canonical Smatch metric that aligns variables of two graphs and assesses triple matches. |
| Outcome: | The proposed metric is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. |
MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning (2022.findings-emnlp)
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| Challenge: | Large-scale pretraining is becoming the norm in Vision-Language (VL) modeling. |
| Approach: | They propose a method for augmenting generative language models with additional modalities using adapter-based finetuning. |
| Outcome: | The proposed method outperforms Frozen on open-ended generative tasks while maintaining the language model weights. |