In this work, we designed, developed and released in production DataQue - a hybrid NLQ (naturallanguage Querying) system for conversational DB querying. We address multiple practical problems that are not accounted f...
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The mission of commonsense knowledge graph completion (CKGC) is to infer missing facts from known commonsense knowledge. CKGC methods can be roughly divided into two categories: triple-based methods and text-based met...
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Reinforcement Learning from Human Feedback significantly enhances naturallanguageprocessing by aligning language models with human expectations. A critical factor in this alignment is the strength of reward models u...
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Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. However, fine-tuning on full parameters solely is inefficient potentially leading to negative interactions among...
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Code-switching (CS) is the process of speakers interchanging between two or more languages which in the modern world becomes increasingly common. In order to better describe CS speech the Matrix language Frame (MLF) t...
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We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in naturallanguage. For example, if the LLM is equally likely to output two contradicting answers to the same que...
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Large Vision-language Models (LVLMs) have become pivotal at the intersection of computer vision and naturallanguageprocessing. However, the full potential of LVLMs' Retrieval-Augmented Generation (RAG) capabilit...
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The telecommunications industry, characterized by its vast customer base and complex service offerings, necessitates a high level of domain expertise and proficiency in customer service center operations. Consequently...
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Adapting Large language Models (LLMs) for agent tasks is critical in developing language agents. Direct Preference Optimization (DPO) is a promising technique for this adaptation with the alleviation of compounding er...
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Handling long input contexts remains a significant challenge for Large language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work aims to address this limitation by intr...
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