Papers by Filip Graliński
Challenging America: Modeling language in longer time scales (2022.findings-naacl)
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Jakub Pokrywka, Filip Graliński, Krzysztof Jassem, Karol Kaczmarek, Krzysztof Jurkiewicz, Piotr Wierzchon
| Challenge: | a dominant approach to solving NLP tasks is pre-training a large neural language model and fine-tuning the model for specific tasks. |
| Approach: | They propose a challenge to train and fine-tune large Transformer models for historical texts . they pre-trained a RoBERTa model from scratch from the historical texts and evaluate them on benchmarks . |
| Outcome: | The proposed ML task is based on OCR-ed clippings from the Chronicling America portal. |
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (2020.findings-emnlp)
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Łukasz Borchmann, Dawid Wisniewski, Andrzej Gretkowski, Izabela Kosmala, Dawid Jurkiewicz, Łukasz Szałkiewicz, Gabriela Pałka, Karol Kaczmarek, Agnieszka Kaliska, Filip Graliński
| Challenge: | Existing methods for detecting text fragments are not suitable for contract discovery, since it requires manual definition of a few examples, followed by conventional information. |
| Approach: | They propose a task where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts. |
| Outcome: | The proposed task differs substantially from conventional NLI and shared tasks on legal information extraction. |
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation (2025.naacl-short)
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| Challenge: | Existing methods to ground large language models fail to adequately attend to all contexts . position bias is hindered by retrieval-augmented generation, which requires constant attention . |
| Approach: | They propose to augment and distill training instances with their perturbed positions to encourage consistent predictions . they also propose to balance COnsistency and Rank Distillation by combining noise-controlled perturbations with augmentation and distillation. |
| Outcome: | The proposed method outperforms existing methods in diverse RAG benchmarks. |
Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)
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| Challenge: | Existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. |
| Approach: | They propose to use inference scaling to aggregate inference calls from the permuted order of retrieved contexts to create a new ranking. |
| Outcome: | The proposed approach improves ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by 7 points. |