Improving RAG: Strategies
LLMs struggle when handling tasks which require extensive knowledge. This limitation highlights the need to supplement LLMs with non-parametric knowledge. This paper Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts analyze the effects of different types of non-parametric knowledge, such as textu... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Google Deepmind used similar idea to make LLMs faster in Accelerating Large Language Model Decoding with Speculative Sampling. Their algorithm uses a smaller draft model to make initial guesses and a larger primary model to validate them. If the draft often guesses right, operations become faster, reducing latency.
There are some people speculating ... See more
There are some people speculating ... See more
muhtasham • Machine Learners Guide to Real World - 2️⃣ Concepts from Operating Systems That Found Their Way in LLMs
By grounding LLMs with use-case-specific information through RAG, the quality and accuracy of responses are improved.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Boosting annotator efficiency with Large Language Models
In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:
Prodigy's... See more
In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:
- NER: ner.llm.correct, ner.llm.fetch
- Spancat: spans.llm.correct, spans.llm.fetch
- Textcat: textcat.llm.correct, textcat.llm.fetch
- Prompt engineering and terms: ab.llm.tournament terms.llm.fetch
Prodigy's... See more