Exploiting Word Sense Disambiguation in Large Language Models for Machine Translation (2025)

Abstract: Machine Translation (MT) has made great strides with the use of Large Language Models (LLMs) and advanced prompting techniques. However, translating sentences with ambiguous words remains challenging, especially when LLMs have limited proficiency in the source language. This paper introduces two methods to enhance MT performance by leveraging the word sense disambiguation capabilities of LLMs. The first method integrates all the available senses of an ambiguous word into the prompting template. The second method uses a pre-trained source language model to predict the correct sense of the ambiguous word, which is then incorporated into the prompting template. Additionally, we propose two prompting template styles for providing word sense information to LLMs. Experiments on the HOLLY dataset demonstrate the effectiveness of our approach in improving MT performance.

## introduction semantic ambiguity has long posed a significant challenge in mt. despite rapid advancements in neural machine translation (nmt), effectively disambiguating and translating ambiguous words remains an unresolved issue. the advent of decoderonly large language models (llms) such as the gpt series @xcite , llama @xcite , and gemma @xcite has shown exceptional capabilities in various natural language processing tasks, including mt. these llms have emerged as promising alternatives, offering performance comparable to traditional nmt models and introducing new paradigms for controlling target outputs. however, due to their predominant pre-training on english-centric language datasets @xcite , llms may lack proficiency in low-resource languages @xcite , making it challenging for them to accurately translate source sen-tences containing ambiguous words in these languages @xcite . this issue is particularly pronounced in small and moderate-sized models (2b, 7b, or 13b) @xcite @xcite . in this study, we investigate the translation capabilities of such llms in handling ambiguous words through prompting techniques, without relying on additional training data. in addition, we present two methods to take advantage of the word-sense disambiguation (wsd) abilities of llms, thus enhancing mt performance. the first method integrates all possible senses of the ambiguous word from a dictionary into the prompting template, encouraging llms to use their internal wsd capabilities to select the appropriate word sense, thus improving translation quality. the second method utilizes an external decoder-only language model pre-trained on a large set of source language data. this model evaluates the perplexities of all sense definitions from a dictionary in the source language and predicts the correct sense with the lowest perplexity. the predicted sense is then incorporated into the prompting template to aid the llms in the translation process. besides, we propose two prompting template styles for each method: natural language style and tagging style. our contributions are as follows: ## related work zero-shot and few-shot prompting have become essential techniques for leveraging llms in mt. zero-shot prompting asks the model to translate directly without examples, while few-shot prompting provides a few examples to guide the model through in-context learning @xcite . previous works @xcite have shown that both methods can achieve competitive results without extensive fine-tuning. although fine-tuning llms in specific language pairs can improve mt @xcite , it demands computational resources and annotated data. more related to our work, pilault et al. (2023) proposed interactive-chain prompting, a promptbased interactive multi-step computation technique that first resolves cross-lingual ambiguities in the input queries and then performs conditional text generation. @xcite presented two techniques to improve the disambiguation abilities of llms, including in-context learning and finetuning. the former involves providing similar ambiguous contexts in the prompt, while the latter involves fine-tuning llms on carefully curated ambiguous datasets through low-rank adaptation. unlike these approaches, our approach takes advantage of the wsd capabilities of llms to improve mt without additional fine-tuning. ## our method given a source sentence containing the ambiguous word in language x, our goal is to use llms to accurately translate the sentence into language y. figure 1 illustrates our approach using the pair (x,y) as (korean, english). following @xcite , we use a basic prompting format: "translate this from korean to english:\nkorean:<source @xmath0:" on llms, as illustrated in block 1 of figure 1 . to enhance llms' ability to translate sentences containing ambiguous words, we use a dictionary to gather all possible senses of the ambiguous word. for example, in block 2 of figure 1 , the word '연 기' has three distinct senses, each with an english translation and a definition in korean. we present two methods to exploit this information for llms. all senses-based prompting. this method incorporates all potential senses of the ambiguous word into the prompting template, utilizing two distinct styles: natural language style (nls) and tagging style (ts). by providing such information, it exploits the wsd ability of llms for ambiguous words, thereby improving mt accuracy. as shown in block 3 of figure 1 , for the nls, we provide all senses of the word '연기' in a natural language format: "hint: '연기' means 'smoke' or 'delay' or 'acting'." in contrast, the ts uses tags to convey the word sense information. for instance, the ambiguous word '연기' is followed by the tag "<@xmath1, delay, @xmath2. ## conclusion this work presents our approach to exploiting the wsd capabilities in llms to enhance the mt performance of sentences with ambiguous words. specifically, we introduce two methods: "all senses-based prompting" and "one predicted sense-based prompting", combined with two styles: nls and ts. experiments on the holly test set highlight the effectiveness of our approach and underscore the importance of exploiting wsd capabilities in llms to improve mt.