Prompt Engineering

Mastering CoT: A Practical Guide to Reasoning Prompts for Large Language Models

Master Chain-of-Thought prompting, the key to unlocking LLMs' reasoning potential. Explore best practices, real-world applications, and ethical considerations. Level up your LLM skills for creative content, problem solving, and more. Discover the future of LLMs, powered by CoT.

Chain-of-Thought (CoT) Prompting: Intro to LLM Reasoning

Understanding the Basics of CoT Prompting:

Imagine you're teaching a child to solve a math problem. Instead of simply giving the answer, you break down the steps involved: "First, identify the numbers. Then, choose the appropriate operation. Finally, perform the calculation and check your answer." This step-by-step approach mirrors the essence of Chain-of-Thought (CoT) prompting.

CoT prompts guide Large Language Models (LLMs) through a series of intermediate reasoning steps instead of just feeding them the raw input and hoping for the best. Think of it as providing the LLM with a roadmap to

Plan-and-Solve Plus (PS+) - A Prompting Framework for Enhanced LLM Reasoning

Plan-and-Solve Plus (PS+): A novel prompting framework for enhanced LLM reasoning. Discover powerful techniques like detailed instructions, self-consistency evaluation, and error analysis to empower your models in zero-shot learning.

Let's take a look at the Plan-and-Solve paper, something I've been meaning to explore in-depth. Sure, its concepts have been rolled into our CRISP prompting framework, but there's more to unpack here.

In this article, we're going to break down what the framework is all about and why it's cool. The academic world is great at coming up with these

Can LLMs Really Explain Themselves? A Look at ChatGPT's Explanatory Abilities

This study explores how LLMs explain their decisions, revealing strengths and weaknesses. Learn about accuracy trade-offs, model behavior, and how to leverage self-explanations for better AI interaction.

A recent study found that Large Language Models (LLMs) like ChatGPT can self-generate feature attribution explanations, but their effectiveness, compared to traditional methods, varies. The study finds no clear winner across different faithfulness metrics, and the explanations show high disagreement. Additionally, the explanation values from LLMs tend to be well-rounded and lack fine-grained variation, suggesting a human-like reasoning approach but raising questions about their precision and utility.

Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations

Large language models (LLMs) such as ChatGPT have demonstrated superior performance on a variety of natural language processing (NLP) tasks including sentiment

ADAPT - Dynamic Decomposition and Planning for LLMs in Complex Decision-Making

The ADAPT methodology: an approach that can Large Language Models' performance in complex decision-making tasks through dynamic task decomposition and planning.

The paper introduces "ADAPT," a novel method for using Large Language Models (LLMs) in decision-making tasks involving planning and adapting to the environment. This approach significantly improves task success rates by dynamically decomposing complex sub-tasks as needed, particularly when standard methods struggle with task complexity.

Key Points

Overview and Purpose: "ADAPT" (As-Needed Decomposition and Planning with Language Models) addresses the limitations of existing LLM-based methods in complex interactive decision-making tasks. It uses recursive decomposition and planning to adapt to task complexity and LLM capabilities.
Existing Approaches and Limitations: Traditional methods use LLMs in two ways: iterative executors and plan-and-execute approaches.

Functional Inference Synthesis: Harnessing the Predictive Power of Large Language Models

How can Words become tools? With the power of AI and a phenomenon know as Functional Inference Synthesis.

With the advent of advanced Large Language Models (LLMs) like GPT-4, a novel phenomenon, Functional Inference Synthesis (FIS), has emerged at the forefront of AI capabilities. FIS is the ability of these models to infer the functionality of tools, concepts, or processes based on their extensive training and sophisticated pattern recognition capabilities. This paper delves into the mechanics of FIS, exploring how LLMs utilize contextual cues and linguistic patterns to generate responses that align with users' expectations of tool or function-based prompts, despite the absence of real computational execution or deep understanding.

Introduction

The landscape of artificial intelligence (AI) has

The Language of Thought - Exploring the Potential of SymboScript Featured Post

Symboscript is a visual language system leveraging emojis and symbols to represent complex conceptual relationships. As an emoji-based combinatorial grammar, it aims to map more closely to innate cognition for deeper meaning representation and insights into human thought processes.

Our innate cognition may rely more heavily on compact visual representations rather than verbal constructs. This perspective is at the core of a fascinating proposed conceptual language system called Symboscript. Symboscript aims to leverage emojis and symbols to encode meaning and relationships directly, mapping more closely to raw human thought.

The Philosophy Underpinning Symboscript

Symboscript is grounded in the belief that reasoning with condensed visual symbols aligns well with natural cognition, whereas verbal language requires intensive translation to concepts. It emphasizes decoding meaning holistically through conceptual linkages instead of just word correlations. This focus on rich representation over terminology could

Mastering CoT: A Practical Guide to Reasoning Prompts for Large Language Models

Chain-of-Thought (CoT) Prompting: Intro to LLM Reasoning

Understanding the Basics of CoT Prompting:

Plan-and-Solve Plus (PS+) - A Prompting Framework for Enhanced LLM Reasoning

Can LLMs Really Explain Themselves? A Look at ChatGPT's Explanatory Abilities

ADAPT - Dynamic Decomposition and Planning for LLMs in Complex Decision-Making

Key Points

Functional Inference Synthesis: Harnessing the Predictive Power of Large Language Models

Introduction

The Language of Thought - Exploring the Potential of SymboScript Featured Post

The Philosophy Underpinning Symboscript

Featured

Reasoners - A New Approach to Smarter AI

Generative AI - The New Compiler

How Prompt Keywords (Magic Words) Optimize Language Model Performance

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