Papers by Agnes Luhtaru
To Err Is Human, but Llamas Can Learn It Too (2024.findings-emnlp)
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| Challenge: | Specifically, we fine-tune Llama 2 LMs for error generation and find that this approach yields synthetic errors akin to human errors. |
| Approach: | They propose to fine-tune Llama 2 LMs for error generation and train GEC Llma models using these artificial errors. |
| Outcome: | The proposed approach outperforms state-of-the-art models with gains ranging between 0.8 and 6 F0.5 points across all languages tested. |
Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer (2024.findings-naacl)
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| Challenge: | Existing methods to adapt pretrained Large Language Models to new lower-resource languages are limited to English. |
| Approach: | They propose to combine cross-lingual instruction-tuning with additional monolingual pretraining to adapt LLMs to new lower-resource languages. |
| Outcome: | The proposed model is the first open-source instruction-following LLM for Estonian . the proposed model improves commonsense reasoning and multi-turn conversation capabilities . |
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |
Multilinguality or Back-translation? A Case Study with Estonian (2024.lrec-main)
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| Challenge: | a limited amount of parallel data is available for machine translation, and synthetic data is often used to improve translation quality. |
| Approach: | They propose a large-scale synthetic corpus of Estonian translations that contains over 1 billion parallel sentences. |
| Outcome: | The proposed model improves the baseline model while maintaining multilinguality . the proposed model is 6 times larger than the Estonian corpus and twice the size of the Estonial part of the CulturaX corpus. |
Limited Linguistic Diversity in Embodied AI Datasets (2026.acl-long)
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Selma Liliane Wanna, Agnes Luhtaru, Jonathan Salfity, Ryan Barron, Juston Moore, Cynthia Matuszek, Mitch Pryor
| Challenge: | Language is a key modality in Vision-Language-Action (VLA) models, yet its linguistic characteristics are poorly documented. |
| Approach: | They propose to audit VLA corpora to characterize what kinds of instructions they contain . they quantify instruction language along complementary dimensions including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. |
| Outcome: | The proposed dataset audit of several widely used VLA datasets shows that many datasets rely on highly repetitive, template-like commands with limited structural variation yielding a narrow distribution of instruction forms. |