Cobus Greyling (4)

Contrastive Chain-Of-Thought Prompting

Contrastive Chain-of-Thought Prompting (CCoT) uses both positive & negative demonstrations to improve LLM reasoning.

OpenAI Structured JSON Output With Adherence

In the past, when using OpenAI’s JSON mode there was no guarantee that the model output will match the specified and...

The Chain-Of-X Phenomenon In LLM Prompting

In a recent post, I discussed the seemingly emergent abilities of LLMs. A new study argues that Emergent Abilities are...

Chain-Of-Note (CoN) Retrieval For LLMs

Chain-of-Note (CoN) is aimed at improving RAG implementations by solving for noisy data, irrelevant documents and out...

Knowledge-Driven Chain-of-Thought (KD-CoT)

The aim of KD-CoT is to introduce faithful reasoning to LLM based Chain-of-Thought reasoning for instances where...

LLM Hallucination Index

Galileo released an LLM Hallucination Index, which makes for very interesting reading. The charts shared considers a...

Are Emergent Abilities In LLMs Inherent Or Merely In-Context Learning?

What are Emergent Abilities? Emergent Abilities is when LLMs demonstrate exceptional performance across diverse tasks...

Chain of Empathy Prompting (CoE)

CoE Prompting enhances Large Language Model (LLM) empathy based on psychotherapy models.

What Are LLMs Good At & When Can LLMs Fail?

As seen in the graph below, the number of Large Language Model (LLM) related studies are increasing immensely....

Distilling the Evaluation of LLMs: Understanding the What, Where, and How

Research focussed on the evaluation of AI models in general and LLMs in specific.

This paper serves as a...