Papers by Philipp Schmidt
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel on English NLU tasks, yet struggle to extend their NLU capabilities to underrepresented languages. |
| Approach: | They integrate machine translation models (MT) directly into LLM backbones via sample-efficient self-distillation. |
| Outcome: | The proposed model outperforms translation-test models on 127 low-resource languages. |
Playpen: An Environment for Exploring Learning From Dialogue Game Feedback (2025.emnlp-main)
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Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Momentè, Luca Franceschetti, Philipp Sadler, Sherzod Hakimov, Alberto Testoni, Raffaella Bernardi, Raquel Fernández, Alexander Koller, Oliver Lemon, David Schlangen, Mario Giulianelli, Alessandro Suglia
| Challenge: | In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning. |
| Approach: | They introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning, direct alignment and reinforcement learning with Group Relative Policy Optimization. |
| Outcome: | The proposed model improves performance on unseen instances, but negatively impacts other skills, while interactive learning shows balanced improvements without loss of skills. |
FABRIC: Fully-Automated Broad Intent Categorization in E-commerce (2025.emnlp-industry)
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| Challenge: | Existing query classification models have excellent predictive performance on single-intent queries, but there is little research on predicting multiple-intentions for broad queries. |
| Approach: | They propose to combine user click data, query-item relevance and LLM judgments to create an automatic method for multi-label e-commerce query classification. |
| Outcome: | The proposed method reduces the ambiguity of the annotations by blending the label assessment from three different sources: user click data, query-item relevance and LLM judgments. |