LLMs
Overview
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Libr-AI • GitHub - Libr-AI/OpenFactVerification: Open-source solution designed to automate the process of verifying factuality
A solution is to self-host an open-sourced or custom fine-tuned LLM. Opting for a self-hosted model can reduce costs dramatically - but with additional development time, maintenance overhead, and possible performance implications. Considering self-hosted solutions requires weighing these different trade-offs carefully.
Developing Rapidly with Generative AI
In the simplest form, we can use the model’s detection confidence to determine a score. But even here there are quite a few options to choose from:
- Lowest confidence - the score is the lowest confidence of all detected objects
- Average confidence - average of all confidences of detected objects
- Minimizing confidence delta - difference between
Active Learning with Domain Experts, a Case Study in Machine Learning
What data to label?
We identified 30 types of tasks that UX professionals used generative AI tools for in their work. We grouped these tasks under four roles: content editor, research assistant, ideation partner, or design assistant.
- Content editor : Generating and editing text, from microcopy to social media posts, based on specifications or copy given by UX
Mingjin Zhang • AI as a UX Assistant
First of all, I'd say you have a bigger problem where your company is trying to find nails with a hammer. That is where your sentiment comes from, and could be an obstacle for both you and the company. It's the same deal when I see people keep on talking about RAG, and nowadays "modular RAG", when really, you could treat everything as a software... See more
r/MachineLearning - Reddit
- Multiple indices. Splitting the document corpus up into multiple indices and then routing queries based on some criteria. This means that the search is over a much smaller set of documents rather than the entire dataset. Again, it is not always useful, but it can be helpful for certain datasets. The same approach works with the LLMs themselves.
Matt Rickard • Improving RAG: Strategies
- Traditional AI - The most secure, understandable, and performant. However, Good implementations of traditional AI require that we define the rules behind the system, which makes it unfeasible for many of the use cases that the other 2 techniques thrive on.
- Supervised Machine Learning- Middle of the road b/w AI and Deep Learning. Good when we have
Devansh • How to Pick between Traditional AI, Supervised Machine Learning, and Deep Learning [Thoughts]
Where would I add generative AI? Generative AI has the ease of accessibility of traditional AI, where people think it is understandable, but it does not have that feature in itself. It also has the opaque and costly nature of DL. Many companies are at the moment rushing into developing things with generative AI without having any prior foundation in AI and any processes set up to manage it: data ops, devops, …
Traditional AI forces you to think about how something works, understand the system, and then define the rules for it. ML lets you use features and feature importance to shortcut some. Deep Learning allows you to brute force it. Generative AI allows you to brute force without any background in DL.
In addition to using our built-in capabilities, you can also define custom actions by making one or more APIs available to the GPT. Like plugins, actions allow GPTs to integrate external data or interact with the real-world. Connect GPTs to databases, plug them into emails, or make them your shopping assistant. For example, you could integrate a... See more
Introducing GPTs
pair-preference-model-LLaMA3-8B by RLHFlow: Really strong reward model, trained to take in two inputs at once, which is the top open reward model on RewardBench (beating one of Cohere’s).
DeepSeek-V2 by deepseek-ai (21B active, 236B total param.): Another strong MoE base model from the DeepSeek team. Some people are questioning the very high MMLU... See more
DeepSeek-V2 by deepseek-ai (21B active, 236B total param.): Another strong MoE base model from the DeepSeek team. Some people are questioning the very high MMLU... See more