My Projects

Mamoru Imade

Prompt Engineer

Product Development Engineer

AI Educator

Phone: +1-503-716-2791

Email: mamoru@nmacad.com

LinkedIn: https://www.linkedin.com/in/mamoru-imade-b9770ba0/

1. Introduction

Driven by a passion for pushing the boundaries of language models (LLMs), I have consistently sought to maximize their potential through meticulous prompt engineering and innovative application development. This page showcases a selection of my projects, highlighting my deep understanding of LLMs and my ability to leverage their power for practical solutions, from research and development to enterprise-level product creation.

2. Comprehensive Evaluation and Fine-Tuning of LLMs

This section delves into my research exploring the capabilities and limitations of various LLMs across different tasks. I believe that a nuanced understanding of prompt engineering is crucial to unlocking the full potential of these models and achieving high performance.

2.1 Project 1: Kanji Conundrum: A Comprehensive Evaluation of Prompt Engineering and Fine-Tuning Strategies for Enhancing Accuracy and Mitigating Biases in Large Language Models' Handling of Japanese Kanji Readings

Challenge

The primary challenge addressed in this project was the persistent issue of inaccurate Kanji readings and inherent biases in LLMs. These models often default to popular but incorrect readings due to biases in their training data. This problem is critical, as inaccuracies can propagate misinformation and reduce the reliability of language technologies. Additionally, detecting and mitigating these biases while ensuring the models can flag uncertainties posed a significant challenge.

Solution

To tackle these issues, I employed a dual approach: advanced prompt engineering and targeted fine-tuning of the GPT-3.5-turbo model. I designed and tested a variety of prompting techniques to guide the LLMs towards accurate Kanji readings, focusing on both simple and complex multi-component prompts. I also fine-tuned the model on a curated dataset emphasizing correct Kanji pronunciations and uncertainty elements. This approach aimed to recalibrate the model's tendency to default to incorrect readings and enhance its ability to detect and respond to uncertainties.

Key Findings

Effectiveness of Complex Multi-Component Prompts: Prompts like Few-shot, Chain-of-Thought, Generated-Knowledge, and Tree-of-Thought significantly improved the model's accuracy in reading Kanji characters and mitigating biases.
Fine-Tuning Enhances Simple Query Prompts: Fine-tuning the model on a dataset focused on correct readings and uncertainties led to substantial performance gains in accuracy and bias mitigation, particularly for simple query prompts.
Uncertainty Detection Capabilities: While complex prompts improved uncertainty detection, there remains a need for further refinement to ensure models can effectively flag ambiguous or incorrect readings.
Bias Recognition and Mitigation: The project highlighted the models' limitations in recognizing and expressing uncertainties when influenced by biased training data, underscoring the importance of tailored training processes.
Cultural and Perspective Biases: The study revealed how different cultural and temporal contexts, as well as the engineers' perspectives, can introduce biases in LLM training, impacting the accuracy and reliability of generated information.

Impact

This project has had a significant impact on the field of natural language processing and education. It provided a new benchmark for the accuracy of Kanji readings in LLMs, offering valuable insights for Japanese language educators on effective prompting techniques. Additionally, it raised critical awareness of the unique challenges posed by Kanji readings and the broader implications of biases in LLMs. The methodologies developed in this project have paved the way for more reliable and responsible language technologies, contributing to ongoing efforts to improve LLMs' accuracy and trustworthiness in handling complex linguistic challenges.

Snapshots

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Link

📝Project Report: https://lip.theaisprint.info/report_llm-evaluation_kanji-reading

3. Application Development Powered by LLMs: Building User-Centric Solutions

This section showcases my ability to translate my prompting skills into tangible applications that address real-world needs. My approach prioritizes a deep understanding of user needs and leverages LLMs throughout the entire product development lifecycle.

LLM-Driven Development Process:

From gathering client requirements to brainstorming, concept generation, script writing, testing, bug fixing, and release, I've incorporated LLMs into some of stages of the development cycle. This strategy allows me to:

Accelerate Development: Leverage LLMs to automate repetitive tasks and generate high-quality code rapidly even without professional coding experiences.
Enhance Creativity: Utilize LLMs as thought partners, sparking innovative solutions and exploring unconventional approaches.
Improve Product Quality: Employ LLMs for rigorous testing and bug detection, ensuring robust and user-friendly applications.