language models struggle in generating code for low-resource programming languages, since these are underrepresented in training data. Either examples or documentation are commonly used for improved code generation. W...
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Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emergin...
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This paper presents LOFI (language, OCR, Form Independent), a pipeline for Document Information Extraction (DIE) in Low-Resource language (LRL) business documents. LOFI pipeline solves language, Optical Character Reco...
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While steady progress has been made on the task of automated essay scoring (AES) in the past decade, much of the recent work in this area has focused on developing models that beat existing models on a standard evalua...
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The rapid growth of open-source language models provides the opportunity to merge model checkpoints, combining their parameters to improve performance and versatility. Advances in transfer learning have led to numerou...
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As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing calibration methods often negatively imp...
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Incorporating naturallanguage rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large language Models (LLMs) performance. However, generating high-quality rationales requ...
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Reasoning is one crucial capability in Large language Models (LLMs), allowing them to perform complex tasks such as solving math problems and multi-step planning. While reasoning capability can emerge in larger models...
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Large language models have emerged as a significant phenomenon due to their ability to produce natural text across various applications. However, the proliferation of generated text raises concerns regarding its poten...
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