Imagine a student taking a closed-book exam, relying solely on what they've memorized. This is how traditional large language models (LLMs) operate, using only the data they were trained on. Retrieval-augmented generation (RAG) changes this by allowing the model, like a student now taking an open-book exam, to
Imagine you've spent countless hours training a large language model, pouring in vast amounts of data and computing power, only to realize that you must fine-tune it for a specific task. Traditional fine-tuning methods often require updating all the model parameters, which can be time-consuming and resource-intensive, especially
The announcement of Mixtral 8x7B and DeepMind's Mixture-of-Depths, among others, has once again made the Mixture of Experts (MoE) architecture for transformers a popular choice in the NLP community. Continuing our previous blog on fine-tuning, we discuss the popular scaling of LLMs via sparse experts, referred to as
LLMs
The alignment of generative models with human feedback has significantly improved the performance of natural language generation tasks. For large language models (LLMs), alignment methods like reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) have consistently worked better than just supervised fine-tuning (SFT) alone based on current
LLMs
Our study aims to explore how different providers use different techniques in order to serve LLMs influencing the overall quality of the models. To achieve this, we conduct empirical tests using various prompts, allowing us to systematically compare the outcomes across various service providers. This approach helps us identify significant
LLMs
Large Language Models (LLMs) are in the spotlight, mainly due to their emerging capabilities. Please refer to our previous blog post on Emergent abilities in LLMs for a deeper dive into this topic. As the old saying goes, You can't make a silk purse out of a sow&
🦹♀️Imagine you have a bunch of superheroes, each trained in a specific skill. Iron Man's got the tech smarts, Black Widow's got the fighting moves, and Captain America...well, Captain America is just generally awesome. But wouldn't it be cool to combine their powers
LLMs
Emergence, a concept well-studied in fields like physics, biology, and mathematics, describes how complex systems can exhibit new, unforeseen properties as their complexity increases. Inspired by P.W. Anderson's influential idea that "More is Different," suggesting that complexity can lead to unexpected behaviors not evident from
LLMs
In this second part of the series, we delve into how LLMs can serve as evaluators, a concept referred to as "LLM as judge." Building on the previous blog, which introduced early benchmarks and metrics for evaluating large language models (LLMs) and highlighted issues with these evaluation methods,
LLMs
The rapid development of Large Language Models (LLMs) also necessitates advancing robust evaluation strategies and systems. For a broader understanding of these technological advancements, look at our previous exploration in The Tiny LLM Revolution. In this blog post, we do a deep dive into existing evaluation benchmarks and discuss future
