Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and th...
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The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data. Existing contamination detection methods are typicall...
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Although Dense Passage Retrieval (DPR) models have achieved significantly enhanced performance, their widespread application is still hindered by the demanding inference efficiency and high deployment costs. Knowledge...
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Iterative data generation and model re-training can effectively align large language models (LLMs) to human preferences. The process of data sampling is crucial, as it significantly influences the success of policy im...
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Despite recent advancements in vision-language models, their performance remains suboptimal on images from non-western cultures, due to underrepresentation in training datasets. Various benchmarks have been proposed t...
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Research on continuous sign language recognition (CSLR) is essential to bridge the communication gap between deaf and hearing individuals. Numerous previous studies have trained their models using the connectionist te...
The objective of the research we present is to remedy the problem of the low quality of language models for low-resource languages. We introduce an algorithm, the Token Embedding Mapping Algorithm (TEMA), that maps th...
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While transformer models exhibit strong capabilities on linguistic tasks, their complex architectures make them difficult to interpret. Recent work has aimed to reverse engineer transformer models into human-readable ...
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We explore the alignment of values in Large language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen *** a diverse set of prompts tailored to ensure response robustn...
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Understanding satire and humor is a challenging task for even current Vision-language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Und...
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