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Technology

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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Factory AI has unveiled ‘Code Droid’, a tool specially developed to improve and automate programming tasks through sophisticated self-reliant features: it has demonstrated its efficiency by scoring 19.27% on full SWE-bench and 31.67% on the lite version of SWE-bench.

Factory AI has unveiled Code Droid, a major innovation in artificial intelligence (AI) designed to streamline and expedite software development processes. As an autonomous AI tool, Code Droid is created to handle a multitude of coding duties based on natural language instructions, assimilating insights from multiple fields, including robotics, machine learning, and cognitive science. The key…

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Factory AI has launched Code Droid, an innovative tool meant to streamline and improve coding with sophisticated self-governing features. It boasts an impressive score of 19.27% on SWE-bench Full and 31.67% on SWE-bench Lite.

Factory AI has launched its state-of-the-art innovation, Code Droid. This artificial intelligence (AI) tool is designed to revolutionize software development by mechanizing and quickening the processes involved. Code Droid is essentially an autonomous system which carries out multiple coding tasks dependent on natural language directions. Its main objective is to automatize mundane programming operations, thus…

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BM25S: An English Programming Package Constituting the BM25 Procedure for Organizing Documents According to a Search Query

The rise of vast data systems has made information retrieval a vital process for numerous platforms, including search engines and recommender systems. This is achieved by finding documents based on their content, a task that presents challenges related to relevance assessment, document ranking, and efficiency. A new Python library named BM25S aims to overcome the…

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BM25S: A Python Toolkit for Executing the BM25 Algorithm to Prioritize Documents According to a Query

In the digital era where data is vast, the importance of information retrieval cannot be overstated, particularly for search engines, recommender systems, and applications that find documents based on their content. Information retrieval involves three fundamental challenges - relevance assessment, document ranking, and efficiency. BM25S is a recently introduced Python library that tackles these challenges…

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LOFT: An All-Inclusive AI Benchmark for Assessing Extensive-Context Language Models

Long-Context Language Models (LCLMs) have emerged as a new frontier in artificial intelligence with the potential to handle complex tasks and applications without needing intricate pipelines that were traditionally used due to the limitations of context length. Unfortunately, their evaluation and development have been fraught with challenges. Most evaluations rely on synthetic tasks with fixed-length…

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DigiRL: An Innovative Self-Sufficient Reinforcement Learning Approach for Training Gadget-Managing Agents

Advancements in vision-language models (VLMs) have enabled the possibility of developing a fully autonomous Artificial Intelligence (AI) assistant that can perform daily computer tasks through natural language. However, just having the reasoning and common-sense abilities doesn't always lead to intelligent assistant behavior. Thus, a method to translate pre-training abilities into practical AI agents is crucial.…

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Emergence of Diffusion-Based Linguistic Models: Evaluating SEDD versus GPT-2

Large Language Models (LLMs) have revolutionized natural language processing, with considerable performance across various benchmarks and practical applications. However, these models also have their own sets of challenges, primarily due to the autoregressive training paradigm which they rely upon. The sequential nature of autoregressive token generation can drastically slow down processing speeds, limiting their practicality…

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