LLMs

Pipeline RobustQA Avg. score Avg. response time (secs) Azure Cognitive Search Retriever + GPT4 + Ada 72.36 >1.0s Canopy (Pinecone) 59.61 >1.0s Langchain + Pinecone + OpenAI 61.42 <0.8s Langchain + Pinecone + Cohere 69.02 <0.6s LlamaIndex + Weaviate Vector Store - Hybrid Search 75.89 <1.0s RAG Google Cloud VertexAI-Search + Bison... See more

How enterprises are using open source LLMs: 16 examples.

Many use Llama-2: Brave, Wells Fargo, IBM, The Grammy Awards, Perplexity, Shopify, LyRise, Niantic....

Quote: “A lot of customer are asking themselves: Wait a second, why am I paying for super large model that knows very little about my business? Couldn’t I just use one of these open-source... See more

Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.

The AI engineering framework

Marvin is a lightweight AI engineering framework for building natural language interfaces that are reliable, scalable, and easy to trust.

Sometimes the most challenging part of working with generative AI is remembering that it's not magic; it's software. It's new, it's nondeterministic, and it's incredibly powerful - but... See more

The way that most RLHF is done to date has the entire response from a language model get an associated score. To anyone with an RL background, this is disappointing, because it limits the ability for RL methods to make connections about the value of each sub-component of text. Futures have been pointed to where this multi-step optimization comes at... See more

To train LLMs, you need data that is:

Large — Sufficiently large LMs require trillions of tokens.

Clean — Noisy data reduces performance.

Diverse — Data should come from different sources and different knowledge bases.

What does clean data look like?

You can de-duplicate data with simple heuristics. The most basic would be removing any exact duplicates... See more

My $0.02 is that a lot of the future research/work there will be figuring out how to identify effective sub-graphs to provide additional context, to avoid having to pass in the entire graph. As well as trying to identify ontology-less structures in real-time, which includes NER and RE, as well as named entity/relationship... See more

However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more