Related papers: Learning migration models for supporting increment…
Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource languages remains a challenging task. To maximize data efficiency…
Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world conditions. Nonetheless, the capacity of LLMs to continuously…
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication…
Large pre-trained language models such as GPT-3, Codex, and Google's language model are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and…
Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along…
This paper presents a hybrid framework for literature reviews that augments traditional bibliometric methods with large language models (LLMs). By fine-tuning open-source LLMs, our approach enables scalable extraction of qualitative…
Despite the growing variety of languages supported by existing multilingual neural machine translation (MNMT) models, most of the world's languages are still being left behind. We aim to extend large-scale MNMT models to incorporate a new…
To alleviate the substantial cost of manually crafting user interface (UI) test cases, UI test migration aims to automatically generate test cases for a target mobile application (app) by adapting those from a source app that shares similar…
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource…
Transformer has demonstrated its great power to learn contextual word representations for multiple languages in a single model. To process multilingual sentences in the model, a learnable vector is usually assigned to each language, which…
The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating…
Moving existing legacy systems to cloud platforms is a difficult and high cost process that may involve technical and non-technical resources and challenges. There is evidence that the lack of understanding and preparedness of cloud…
Despite advances in the multilingual capabilities of Large Language Models (LLMs), their performance varies substantially across different languages and tasks. In multilingual retrieval-augmented generation (RAG)-based systems, knowledge…
Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical models of API usage. This paper addresses a different API…
With the promise of accelerating software development, low-code platforms (LCPs) are becoming popular across various industries. Nevertheless, there are still barriers hindering their adoption. Among them, vendor lock-in is a major concern,…
Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified…
The adoption of microservice architecture has seen a considerable upswing in recent years, mainly driven by the need to modernize legacy systems and address their limitations. Legacy systems, typically designed as monolithic applications,…
Migration policies in distributed evolutionary algorithms has not been an active research area until recently. However, in the same way as operators have an impact on performance, the choice of migrants is due to have an impact too. In this…
Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize improving overall performance metrics, paying less…
The Harland document management system implements a data model in which document (object) structure can be altered by mixin-style multiple inheritance at any time. This kind of structural fluidity has long been supported by knowledge-base…