## 1 Introduction  
Morphological disambiguation (MD) is a long-standing problem in processing text for morphologically complex languages. It is similar to part-of-speech (POS) tagging [1]; however, for MD, not only the POS tag but the lemma/root along with its corresponding morphological tags should be correctly predicted. Considering the analysis involves various components like root forms, part-of-speech tags, and morpheme chains, treating MD as a straightforward tagging task introduces complexity, leading to an expansive tagset with sparse data points. To address this issue, several strategies have been proposed. A common approach is to decompose the tag sequence into smaller, more manageable segments. For example, in the HMM-based method proposed by [PERSON] et al. [2], the analysis is broken down into smaller parts like inflectional groups. This method operates under the assumption that the tags in the current analysis depend only on the previous one, simplifying the task of disambiguation. However, this assumption has significant drawbacks. First, it limits the model's ability to capture long-term dependencies, which are essential for this type of task. Second, despite the decomposition, the overall tagset remains quite large, making the approach less efficient. To alleviate this issue, a voted perception approach is proposed in [3]; instead of using a certain part of the analysis, the author proposed a set of features and sought to represent a sequence analysis with feature vectors. The approach utilized tri-gram decoding, which relaxes the previous assumption and, compared to the HMM-based approach, may better capture longer dependencies between tags. The underlying hypothesis of this approach is that the model will maximize the objective function, which ensures the feature vectors from the correct path of analyses obtain larger values than those in the non-correct path of analyses. This is a discrete feature-based approach, which requires manual feature engineering and it still cannot capture long-term dependencies. To address this issue, a deep learning-based approach was proposed [4], which is currently considered the best for this task. The authors segment an analysis into (i) the root, (ii) its POS and (iii) the morpheme chain (MC), then use a nonlinear layer to calculate a dense representation for analysis. In order to capture the long-term dependencies, a bi-directional long short-term memory (LSTM) method was applied for context learning from sentences. Another character-level LSTM is utilized for capturing word internal features. Combing the fine-grained information from characters, the contextualized representation obtained for each word, and guided by the intuition that the correct analysis should be most similar to the context's representation, a dot product of two representations is computed to perform disambiguation. One disadvantage of this approach is that it uses a binary vector to calculate analysis embeddings, which are not in continuous space. Another limitation is that it relies on a biLSTM to compute the contextual representation, which may not fully capture the complexities of language use in syntactic and semantic contexts as effectively as embeddings generated from large language models. With the advent of large language models (LLMs) [5; 6; 7], many results for downstream tasks of natural language processing (NLP) [8; 9] have improved significantly. However, the impact of pre-trained large language models on the performance of MD remains unclear. In this paper, a contrastive learning approach for MD using LLM is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct analysis and contextual embeddings while maintaining a margin between correct and incorrect embeddings. One of the aims of this work is to analyze the effectiveness of LLMs on MD and how the knowledge encoded in LLMs can be transferred to low-resource languages to improve model performance.