Papers by Klaudia Bałazy
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks (2023.eacl-main)
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| Challenge: | Existing gradient-based attacks quantize all tokens in a text at once, which creates a significant gap between adversarial loss for continuous and discrete text representations. |
| Approach: | They propose a gradient-based attack that quantizes tokens one by one and reoptimizes adversarial example after each quantization. |
| Outcome: | The proposed method outperforms other approaches on various natural language processing tasks. |
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA (2025.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models . standard LoRA lacks mechanisms for uncertainty quantification, leading to overconfident and poorly calibrated models. |
| Approach: | They propose a parameter-efficient Bayesian LoRA method that decomposes weight updates into low-rank matrices. |
| Outcome: | The proposed method achieves strong performance with improved calibration and generalization while maintaining computational efficiency. |
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models (2023.findings-eacl)
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| Challenge: | Recent transformer language models achieve outstanding results on many downstream tasks, but their enormous size often makes them impractical on memory-constrained devices. |
| Approach: | They propose an offline compression approach that reduces the complexity of the model by enabling collaboration between modules. |
| Outcome: | The proposed approach outperforms commonly used factorization-based offline compression methods on various NLP tasks. |