Training on large amounts of rationales (i.e., CoT Fine-tuning) has been found effective for improving mathematical reasoning of large language models (LLMs). However, acquiring human-authored solutions or augmenting ...
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Scientific reasoning poses an excessive challenge for even the most advanced Large language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented s...
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Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downst...
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Recent research has focused on examining Large language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administra...
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Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses on broadening the training set with va...
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This study is the first to explore whether multi-modal large language models (LLMs) can align their behaviors with visual personas, addressing a significant gap in the literature that predominantly focuses on text-bas...
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Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, valu...
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While effective data visualization is crucial to present complex information in academic research, its creation demands significant expertise in both data management and graphic *** explore the potential of using Visi...
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Large language Models (LLMs) can generate the same sequences contained in the pre-train corpora, known as memorization. Previous research studied it at a macro level, leaving micro yet important questions under-explor...
Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Recent advances have highlighted ef...
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