Papers by Robert Belanec
Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. |
| Approach: | They use a multilingual LLM to generate synthetic datasets covering 11 languages and 4 classification tasks and use them to train smaller models. |
| Outcome: | The proposed model outperforms the large generator in low-resource languages and tasks. |
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation (2024.findings-emnlp)
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| Challenge: | Despite the success of fine-tuning, it still displays model performance instability, especially with limited data. |
| Approach: | They propose a new mitigation strategy that leverages the strengths of ensembling, noise regularisation and model interpolation while retaining computational efficiency. |
| Outcome: | The proposed mitigation strategy outperforms the best performing mitigation strategy (Ensemble) while using only a fraction of its cost. |
PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models (2026.eacl-demo)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs). |
| Approach: | They propose a framework for efficient fine-tuning Large Language Models (LLMs) they aim to train only a small percentage of the full model's parameters . |
| Outcome: | Xu et al., 2023; Ding e t al, 2024; Lialin e al. 2023) show that using PEFT methods can improve performance. |
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark (2026.eacl-long)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods reduce the number of trainable parameters while maintaining strong downstream performance. |
| Approach: | They propose a unified benchmark for evaluating diverse PEFT methods on autoregressive LLMs. |
| Outcome: | The proposed methods reduce trainable parameters while maintaining strong downstream performance. |