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MasterGo AI is an intelligent assistant based on artificial intelligence technology with powerful functions and advantages. It helps users handle various tasks efficiently and provides personalized solutions. MasterGo AI is reasonably priced and offers flexible targeting options for both individual and business users.
MasterGo AI is suitable for various scenarios, including personal work, business management, academic research, etc.
Scheduling and reminders with MasterGo AI
Enterprises leverage MasterGo AI for project management and team collaboration
Scholars use MasterGo AI for literature search and analysis
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Garden of AI is a new type of assistant that better understands your needs and handles any task. Talk to it like you would a normal person, instead of robotic prompts, try a more natural tone, and if there's something you don't like, just tell it! Garden of AI automatically figures out the steps required to execute your command. It is a proof-of-concept product and may have some bugs and glitches. For some queries, you may need an API key to continue using it.
Radal is a no-code platform that fine-tunes small language models using your own data, for startups, researchers, and enterprises that need custom AI without the complexity of MLOps. Its main advantage is that it enables users to quickly train and deploy custom language models, lowering the technical threshold and saving time and costs.
Gitee AI brings together the latest and hottest AI models, provides one-stop services for model experience, inference, training, deployment and application, provides abundant computing power, and is positioned as the best AI community in China.
MouSi is a multi-modal visual language model designed to address current challenges faced by large-scale visual language models (VLMs). It uses integrated expert technology to collaborate the capabilities of individual visual encoders, including image-text matching, OCR, image segmentation, etc. This model introduces a fusion network to uniformly process outputs from different vision experts and bridge the gap between image encoders and pre-trained LLMs. In addition, MouSi also explored different position encoding schemes to effectively solve the problems of position encoding waste and length limitation. Experimental results show that VLMs with multiple experts exhibit superior performance than isolated visual encoders, and obtain significant performance improvements as more experts are integrated.
OpenAI Embedding Models is a series of new embedding models, including two new embedding models and updated GPT-4 Turbo preview models, GPT-3.5 Turbo models, and text content review models. By default, data sent to the OpenAI API is not used to train or improve OpenAI models. New embedding models with lower pricing include the smaller, more efficient text-embedding-3-small model and the larger, more powerful text-embedding-3-large model. An embedding is a sequence of numbers that represents a concept in something like natural language or code. Embeddings make it easier for machine learning models and other algorithms to understand the relationships between content and perform tasks such as clustering or retrieval. They provide support for knowledge retrieval in the ChatGPT and Assistants APIs, as well as many retrieval augmentation generation (RAG) development tools. text-embedding-3-small is a new efficient embedding model. Compared with its predecessor text-embedding-ada-002 model, it has stronger performance. The average MIRACL score increased from 31.4% to 44.0%, while the average score in the English task (MTEB) increased from 61.0% to 62.3%. Pricing for text-embedding-3-small is also 5x lower than the previous text-embedding-ada-002 model, from $0.0001 per thousand tags to $0.00002. text-embedding-3-large is a new generation of larger embedding models, capable of creating embeddings of up to 3072 dimensions. With stronger performance, the average MIRACL score increased from 31.4% to 54.9%, while the average score in MTEB increased from 61.0% to 64.6%. text-embedding-3-large is priced at $0.00013/thousand marks. Additionally, we support native functionality for shortening embeddings, allowing developers to trade off performance and cost.
Adept Fuyu-Heavy is a new multi-modal model designed specifically for digital agencies. It performs well in multimodal reasoning, particularly in UI understanding, while also performing well on traditional multimodal benchmarks. Furthermore, it demonstrates our ability to extend the Fuyu architecture and obtain all associated benefits, including processing images of arbitrary sizes/shapes and efficiently reusing existing transformer optimizations. It also has the ability to match or exceed the performance of models of the same computational level, albeit requiring some of the capacity to be devoted to image modeling.
Meta-Prompting is an effective scaffolding technique designed to enhance the functionality of language models (LM). This method transforms a single LM into a multi-faceted commander, adept at managing and integrating multiple independent LM queries. By using high-level instructions, meta-cues guide LM to decompose complex tasks into smaller, more manageable subtasks. These subtasks are then handled by different "expert" instances of the same LM, each operating according to specific customized instructions. At the heart of this process is the LM itself, which, as the conductor, ensures seamless communication and effective integration between the outputs of these expert models. It also leverages its inherent critical thinking and robust validation processes to refine and validate the final results. This collaborative prompting approach enables a single LM to simultaneously act as a comprehensive commander and a diverse team of experts, significantly improving its performance in a variety of tasks. The zero-shot, task-agnostic nature of meta-cues greatly simplifies user interaction, eliminating the need for detailed task-specific instructions. Furthermore, our research shows that external tools, such as the Python interpreter, can be seamlessly integrated with the meta-hint framework, thereby broadening its applicability and utility. Through rigorous experiments with GPT-4, we demonstrate that meta-cueing outperforms traditional scaffolding methods: averaged across all tasks, including the 24-point game, One Move General, and Python programming puzzles, meta-cueing using the Python interpreter feature outperforms standard prompts by 17.1%, is 17.3% better than expert (dynamic) prompts, and is 15.2% better than multi-personality prompts.
WARM is a solution for aligning large language models (LLMs) with human preferences through the Weighted Average Reward Model (WARM). First, WARM fine-tunes multiple reward models and then averages them in the weight space. Through weighted averaging, WARM improves efficiency compared to traditional predictive ensemble methods, while improving reliability under distribution shifts and preference inconsistencies. Our experiments show that WARM outperforms traditional methods on summarization tasks, and using optimal N and RL methods, WARM improves the overall quality and alignment of LLM predictions.
ReFT is a simple and effective way to enhance the inference capabilities of large language models (LLMs). It first warms up the model through supervised fine-tuning (SFT), and then uses online reinforcement learning, specifically the PPO algorithm in this article, to further fine-tune the model. ReFT significantly outperforms SFT by automatically sampling a large number of reasoning paths for a given problem and naturally deriving rewards from real answers. The performance of ReFT may be further improved by incorporating inference-time strategies such as majority voting and re-ranking. It is important to note that ReFT improves by learning the same training problem as SFT without relying on additional or enhanced training problems. This shows that ReFT has stronger generalization ability.
Contrastive Preference Optimization is an innovative approach to machine translation that significantly improves the performance of ALMA models by training the model to avoid generating translations that are merely adequate but not perfect. This method can meet or exceed the performance of WMT competition winners and GPT-4 on WMT'21, WMT'22 and WMT'23 test datasets.
Zhipu AI released GLM-4 and CogView3 at the first Technology Open Day. The overall performance of GLM-4 has been improved by nearly 60%, supporting longer context, stronger multi-modal support and faster reasoning. CogView3 approaches the multi-modal generation capabilities of DALL·E 3. The product is positioned as the next generation of base model and image generation AI.
Detection is the industry's leading artificial intelligence (AI) tool platform, providing AI dialogue, AI painting, AI digital human and other products. Committed to better interaction between machines and people, the ultimate goal is to let us hand over work to artificial intelligence and enjoy a better life. The product is positioned to provide users with an intelligent and efficient tool platform to meet their needs in dialogue, painting, digital humans, etc.
Chain-of-Table is a reasoning linked list framework for table understanding, specially designed to handle tasks such as table-based question answering and fact verification. It uses tabular data as part of the reasoning chain and guides large language models to perform operation generation and table updating by learning in context, thereby forming a continuous reasoning chain that demonstrates the reasoning process for a given tabular problem. This chain of reasoning contains structured information about intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on multiple benchmarks including WikiTQ, FeTaQA and TabFact.
DocGraphLM is a document graph language model for information extraction and question answering. It uses advanced visual rich document understanding technology, combining pre-trained language models and graph semantics. It is unique in that it proposes a joint encoder architecture to represent documents and adopts a novel link prediction method to reconstruct the document graph. DocGraphLM predicts the direction and distance between nodes via a convergent joint loss function, prioritizing neighborhood recovery and downweighting remote node detection. Experiments on three SotA datasets show that employing graphical features can achieve consistent improvements in information extraction and question answering tasks. Furthermore, we report that employing graphical features accelerates convergence during training, even though these features are only constructed via link prediction.
TinyGPT-V is an efficient multi-modal large-scale language model implemented by using a small backbone network. It has powerful language understanding and generation capabilities and is suitable for various natural language processing tasks. TinyGPT-V uses Phi-2 as a pre-trained model, which has excellent performance and efficiency.