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Editors Pick

Microsoft AI Unveils Guided Pre-Training: Boosting Pre-Training of Language Model with Supervised Multitask Learning.

Instruction Pre-Training (InstructPT) is a new concept co-developed by Microsoft Research and Tsinghua University that is revolutionizing the task of pre-training language models. This novel approach stands out from traditional Vanilla Pre-Training techniques, which solely rely on unsupervised learning from raw corpora. InstructPT builds upon the Vanilla method by integrating instruction-response pairs, which are derived…

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Delphi-2M: An Adapted GPT Structure for Predicting Future Health Conditions Using Historical Medical Data

Artificial Intelligence has significant potential to revolutionize healthcare by predicting disease progression using extensive health records, enabling personalized care. Multi-morbidity, the presence of multiple acute and chronic conditions in a patient, is an important factor in personalized healthcare. Traditional prediction algorithms often focus on specific diseases, but there is a need for comprehensive models that…

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Delphi-2M: An Altered GPT Structure for Predicting Future Health Using Previous Medical Records

Artificial Intelligence (AI) models have huge potential to predict disease progression through analysis of health records, facilitating a more personalised healthcare service. This predictive capability is crucial in enabling more proactive health management of patients with chronic or acute illnesses related to lifestyle, genetics and socio-economic factors. Despite the existence of various predictive algorithms for…

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Investigating the Potentials and Obstacles of Inductive Reasoning Out of Context in Extensive Language Models: Impact on the Safety of Artificial Intelligence.

Large Language Models (LLMs), significant advancements in the field of artificial intelligence (AI), have been identified as potential carriers of harmful information due to their extensive and varied training data. This information can include instructions on creating biological pathogens, which pose a threat if not adequately managed. Despite efforts to eliminate such details, LLMs can…

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MIPRO: An Innovative Optimizer Surpassing Benchmark Performances on Five out of Six Varied Language Model LM Applications Leveraging a Top-Tier Open-Source Model (Llama-3-8B) with a 12.9% Accuracy Increase

Language models (LMs) are a vital component of complex natural language processing (NLP) tasks. However, optimizing these models can be a tedious and manual process, hence the need for automation. Various methods to optimize these programs exist, but they often fall short, especially when handling multi-stage LMs that have diverse architectures. A group of researchers…

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Cephalo: A Collection of Open-Source Extensive Language Models for Multimodal Vision (V-LLMs), Specifically Designed with the Perspective of Bio-Inspired Design.

Materials science is a field of study that focuses on understanding the properties and performance of various materials, with an emphasis on innovation and the creation of new material for a range of applications. Particular challenges in this field involve integrating large amounts of visual and textual data from scientific literature to enhance material analysis…

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Cephalo: An Array of Open-Source, Multimodal Vision, Extensive Linguistic Models (V-LLMs) Particularly for Bio-Inspired Design Applications

Materials science focuses on the study of materials to develop new technologies and improve existing ones. Most researchers in this realm use scientific principles such as physics, chemistry, and understanding of engineering. One major challenge in materials science is collating visual and textual data for analysis to improve material inventions. Traditional methods rarely combine both…

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APEER: An Innovative Automated Method for Prompt Engineering Algorithm to Rank Relevance of Text Passages

Large Language Models (LLMs) for Information Retrieval (IR) applications, such as those used for web search or question-answering systems, currently base their effectiveness on human-crafted prompts for zero-shot relevance ranking – ranking items by how closely they match the user's query. Manually creating these prompts for LLMs is time-consuming and subjective. Additionally, this method struggles…

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APEER: A New Innovative Algorithm for Automatic Prompt Engineering Aimed at Passage Relevance Ranking

In the field of information retrieval (IR), large language models (LLMs) often require human-created prompts for precise relevance ranking. This demands a considerable amount of human effort, increasing the time consumption and subjectivity of the process. Current methods, such as manual prompt engineering, are effective but still time-intensive and plagued by inconsistent skill levels. Current…

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A team of scholars from the University of Maryland has presented the GenQA Instruction Dataset: a tool for automatically developing large-scale instruction datasets for the improvement and diversification of AI models.

Natural language processing plays a crucial role in refining language models for specified tasks by training AI models on vast and detailed datasets. However, the creation of these extensive datasets is arduous and costly, often requiring substantial human effort, and has, thus, resulted in a gap between academic research and industrial applications. The major obstacle…

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Toucan TTS: A sophisticated Text-to-Speech toolbox, authorized by MIT license, with the capability of speech generation in over 7000 languages.

The Institute for Natural Language Processing (IMS) at the University of Stuttgart, Germany, has made a significant contribution to the field of text-to-speech (TTS) technology with the introduction of ToucanTTS. Supported by PyTorch and Python, ToucanTTS brings to the table a language support encompassing more than 7,000 languages, marking a strong influence on the multilingual…

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Perpendicular Routes: Streamlining Escapes in Linguistic Models

Safeguarding the ethics and safety of large language models (LLMs) is key to ensuring their use doesn't result in harmful or offensive content. In examining why these models sometimes generate unacceptable text, researchers have discovered that they lack reliable refusal capabilities. Consequently, this paper explores ways in which LLMs can deny certain content types and…

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