Papers by Cedric Lothritz
Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain (2023.findings-emnlp)
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| Challenge: | Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. |
| Approach: | They compare performance of financial BERT-like models to their fully fine-tuned counterparts by using parameter-efficient tuning methods. |
| Outcome: | The proposed approaches match full fine-tuning performance on common NLP tasks, but are less studied in finance. |
Testing Low-Resource Language Support in LLMs Using Language Proficiency Exams: the Case of Luxembourgish (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are used in research and society at large, but are mostly developed with English-speaking users in mind. |
| Approach: | They investigate the viability of language proficiency exams as evaluation tools for Luxembourgish . large models such as Claude and DeepSeek-R1 typically achieve high scores . |
| Outcome: | The proposed models can predict performance in Luxembourgish language tests. |
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance (2024.acl-short)
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| Challenge: | Abstract Syntax Tree (AST) editing distance is a new evaluation metric for code generation tasks. |
| Approach: | They propose, optimize, and publish an enhanced version of Tree Similarity of Edit Distance (TSED) based on AST editing distance and prompt-based GPT similarity scores. |
| Outcome: | The proposed metric is an enhanced version of Tree Similarity of Edit Distance (TSED) it is compared to BLEU score, execution match, and Jaccard similarity across languages. |
ltzGLUE: Luxembourgish General Language Understanding Evaluation (2026.findings-acl)
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Alistair Plum, Felicia Körner, Anne-Marie Lutgen, Laura Bernardy, Fred Philippy, Emilia Milano, Nils Rehlinger, Cedric Lothritz, Tharindu Ranasinghe, Barbara Plank, Christoph Purschke
| Challenge: | ltzGLUE is the first official NLU benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. |
| Approach: | They propose a new natural language understanding (NLU) benchmark for Luxembourgish based on the popular GLUE benchmark for English. |
| Outcome: | The proposed model performs well across many languages and is based on the GLUE benchmark for English. |
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish (2022.lrec-1)
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Cedric Lothritz, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Tegawende Bissyande, Jacques Klein, Andrey Boytsov, Clément Lefebvre, Anne Goujon
| Challenge: | Pre-trained Language Models such as BERT are ubiquitous in NLP but are scarce for low-resource languages such as Luxembourgish. |
| Approach: | They propose a BERT model for Luxembourgish language that they use to augment pre-training datasets by partially translating text data from a closely related language. |
| Outcome: | The proposed model outperforms the baseline model and the mBERT model in Luxembourgish. |
CodeAgent: Autonomous Communicative Agents for Code Review (2024.emnlp-main)
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Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, Tegawendé Bissyandé
| Challenge: | Existing methods for code review rely on single input-output generative models and thus lack the collaborative nature of code review. |
| Approach: | They propose a multi-agent Large Language Model (LLM) system for code review automation that incorporates a supervisory agent to ensure that all the agents’ contributions address the initial review question. |
| Outcome: | The proposed system detects inconsistencies between code changes and commit messages, identify vulnerabilities, validates code style adherence, and suggests code revisions. |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). |
| Approach: | They compare three transformer-based names to two non-transformer-based ones . they find transformer-derived models incrementally outperform non-tranformer models . |
| Outcome: | The proposed models outperform the studied models in most domains with respect to the F1 score. |