Papers by Ljiljana Dolamic
Early Guessing for Dialect Identification (2022.findings-emnlp)
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| Challenge: | Current research on dialect identification is model-centric, focusing on performance. |
| Approach: | They propose a data-centric approach to find the shortest input needed to make a plausible guess. |
| Outcome: | The proposed method generalizes across dialects and datasets with two shortening criteria. |
Low-Resource Languages LLM Disinformation is Within Reach: The Case of Walliserdeutsch (2025.findings-emnlp)
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| Challenge: | a low-resource language lacks fluidity, but its capabilities can be leveraged. |
| Approach: | They investigate whether a moderately sophisticated attacker can perform an impersonation attack in the Walliserdeutsch dialect . |
| Outcome: | The proposed attack is performed in the Walliserdeutsch dialect, a low-resource language . the findings highlight the urgency of LLM detectability research in low-source languages. |
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) pre-trained on massive text data in many languages are preferred solution for various Natural Language processing tasks. |
| Approach: | They compare tokenization parity and information parity as representational biases in pre-trained models . they find TP is better predictor of performance on tasks reliant on syntactic and morphological cues . |
| Outcome: | The proposed model improves on dialect classification, topic classification, and extractive question answering tasks. |
BUST: Benchmark for the evaluation of detectors of LLM-Generated Text (2024.naacl-long)
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| Challenge: | Using the benchmark, we evaluated 5 detectors and found substantial performance variance across tasks. |
| Approach: | They propose to evaluate detectors of texts generated by instruction-tuned large language models (LLMs) using a benchmark dataset, they evaluated 5 detectors and found substantial performance variance across tasks. |
| Outcome: | The proposed benchmarks evaluated 5 detectors and found substantial performance variance across tasks. |
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation (2024.eacl-long)
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| Challenge: | Extensive research has been devoted to adversarial attacks against NMT models . perturbations of inputs can mislead the target model, resulting in incorrect outputs . |
| Approach: | They propose an adversarial attack framework that alters the class of output translations of an NMT model and a classifier to craft adversarials whose translations belong to a different class . |
| Outcome: | The proposed approach has a more substantial effect on the translation by altering the overall meaning, which leads to a different class determined by an oracle classifier. |