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pro vyhledávání: '"Hahnloser Richard H R"'
MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions
Large Language Models (LLMs) produce eloquent texts but often the content they generate needs to be verified. Traditional information retrieval systems can assist with this task, but most systems have not been designed with LLM-generated queries in m
Externí odkaz:
http://arxiv.org/abs/2408.14623
Autor:
Schulthess, Lukas, Marty, Steven, Dirodi, Matilde, Rocha, Mariana D., Rüttimann, Linus, Hahnloser, Richard H. R., Magno, Michele
Animal vocalisations serve a wide range of vital functions. Although it is possible to record animal vocalisations with external microphones, more insights are gained from miniature sensors mounted directly on animals' backs. We present TinyBird-ML;
Externí odkaz:
http://arxiv.org/abs/2407.21486
We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer. By prefixing each text block in the parsed document with the provided question and question type, MemSum-
Externí odkaz:
http://arxiv.org/abs/2310.06436
Autor:
Gu, Nianlong, Hahnloser, Richard H. R.
Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these processes inter-operable, natural language processing (NLP) provides opportun
Externí odkaz:
http://arxiv.org/abs/2306.03535
The abstracts of scientific papers consist of premises and conclusions. Structured abstracts explicitly highlight the conclusion sentences, whereas non-structured abstracts may have conclusion sentences at uncertain positions. This implicit nature of
Externí odkaz:
http://arxiv.org/abs/2305.11553
Event detection improves when events are captured by two different modalities rather than just one. But to train detection systems on multiple modalities is challenging, in particular when there is abundance of unlabelled data but limited amounts of
Externí odkaz:
http://arxiv.org/abs/2211.09376
Autor:
Gu, Nianlong, Hahnloser, Richard H. R.
Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript. However, a rigid citation generation process is at odds with an author's desire to control specific attributes, such as 1) the cita
Externí odkaz:
http://arxiv.org/abs/2211.07066
The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context. To balance the tradeoff between speed and accuracy of citation recommendation in the context of
Externí odkaz:
http://arxiv.org/abs/2112.01206
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects se
Externí odkaz:
http://arxiv.org/abs/2107.08929
Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant pa
Externí odkaz:
http://arxiv.org/abs/2005.04961