The research on Chain-Of-Thought Prompting (CoT) was published on 10 Jan 2023, and during the last year, there has been a significant number of prompting techniques premised on CoT. Launching the Chain-Of-X phenomenon. — With CoT being such a pivotal moment in LLM prompting, what are the underlying principles and structure constituting CoT prompting, and what is the anatomy of CoT Prompting? Introduction to Chain-of-Thought (COT)

In this article I explained the basics of string tokenisation for different OpenAI models, and I share a complete working notebook to compare token input and output lengths of different OpenAI models. — Introduction OpenAI’s extensive large language models operate by converting text into tokens. These models learn to discern the statistical connections among these tokens and excel in predicting the subsequent token in a sequence.

In this article I explain OpenAI token usage for model input, output & fine-tuning. And how to convert text into tokens to calculate token-use cost. — I would like to start with a few assumptions I hold regarding the LLMs and production implementations…and I truly welcome any correction and examples illustrating the contrary. 🙂 Assumptions I get the impression that most LLM use-cases and implementations are focussed on experimentation and design-time use-case implementations (as opposed to run-time…

Contrastive Chain-of-Thought Prompting (CCoT) uses both positive & negative demonstrations to improve LLM reasoning. — Introduction to Chain-of-Thought (COT) Of Late there has been a number of Chain-Of-X illustrating how Large Language Models (LLMs) are capable of decomposing complex problems into a series of intermediate steps. This lead to a phenomenon which some call Chain-Of-X. This basic principle was first introduced by the concept of Chain-Of-Thought(CoT) prompting…

The aim of KD-CoT is to introduce faithful reasoning to LLM based Chain-of-Thought reasoning for instances where Knowledge-intensive Question Answering is required. — This study strongly reminds of the Chain-Of-Knowledge Prompting approach. Reoccurring Themes Considering recent studies on prompting approaches for LLMs, there are a number of reoccurring themes… The one theme is that Emergent Abilities are not hidden or unpublished LLM capabilities which are just waiting to be discovered, but rather the phenomenon of…

Coral is a LLM-based chat User Interface from Cohere and competes with products like OpenAI ChatGPT, HuggingFace HuggingChat… — Taking A Step Back The term ChatGPT has become ubiquitous whenever reference is made to Large Language Model (LLM) functionality. To understand the category Coral falls into, it makes sense to do a quick overview of the three categories of LLM services. Raw Models There are a number of open sourced LLMs, like Bloom, Meta’s Llama…

Chain-Of-Knowledge (CoK) is a new prompting technique which is well suited for complex reasoning tasks. — However, the challenge with CoK prompting is that it demands complexity when implemented in a production environment. Overview As seen in the graphic above, creating a working example of CoK in a prompt playground is straight forward. …

This feature is separate to function calling. When calling the models “gpt-4–1106-preview” or “gpt-3.5-turbo-1106” the model response can be set to JSON. However there are a number of considerations… — Some Considerations When using function calling, the JSON mode is always on, however with Chat Completions, the JSON flag needs to be set: response_format={ “type”: “json_object” }. With function calling, a JSON schema or structure is created by the user, against which the generated response is matched and the JSON fields are…

In a recent post, I discussed the seemingly emergent abilities of LLMs. A new study argues that Emergent Abilities are not hidden or unpublished model capabilities which are just waiting to be discovered, but rather new approaches of In-Context Learning which are being built. — A number of recent papers have shown that on user instruction to do so, Large Language Models (LLMs) are capable of decomposing complex problems into a series of intermediate steps.