Papers by Dimitris Alikaniotis
mEdIT: Multilingual Text Editing via Instruction Tuning (2024.naacl-long)
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| Challenge: | mEdIT is a multi-lingual extension to CoEdit for writing assistance. |
| Approach: | They propose to train multi-lingual large language models (LLMs) by fine-tuning them via instruction tuning. |
| Outcome: | The proposed model performs well on multilingual text editing benchmarks and generalizes well to new languages. |
Characterizing the Confidence of Large Language Model-Based Automatic Evaluation Metrics (2024.eacl-short)
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| Challenge: | Recent studies have focused on using Large Language Models (LLMs) to evaluate NLP tasks automatically. |
| Approach: | They characterize LLM evaluators’ confidence in ranking candidate NLP models and develop a configurable Monte Carlo simulation method to compensate for loss of correlation. |
| Outcome: | The proposed method can reach 95% confidence rankings of candidate models with reasonable evaluation set sizes. |
Adversarial Grammatical Error Correction (2020.findings-emnlp)
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| Challenge: | Experimental results show that adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines. |
| Approach: | They propose an adversarial approach to Grammatical Error Correction using a transformer-based model and a sentence-pair classification model. |
| Outcome: | The proposed approach achieves competitive GEC quality compared to baselines. |
Source Identification in Abstractive Summarization (2024.eacl-short)
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| Challenge: | Existing studies define input sentences that contain essential information in the generated summary as source sentences. |
| Approach: | They define input sentences that contain essential information in the generated summary as source sentences and analyze the source sentences to determine how abstractive summaries are made. |
| Outcome: | The proposed method performs well in abstractive settings, while similarity-based methods perform robustly in extractive settings. |