MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
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AI agent helping write and fix code, running tests and iterating till code passes tests or matches designs. Uses OpenAI API and aims to make coding easier.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
This paper introduces Cross-Layer Attention (CLA), an extension of Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) for reducing the size of the key-value cache in transformer-based autoregressive large language models (LLMs). The authors demonstrate that CLA can reduce the cache size by another 2x while maintaining nearly the same accuracy as unmodified MQA, enabling inference with longer sequence lengths and larger batch sizes.
