Zindi ITU Challenge Docs
  • Overview
    • 💡Introduction
    • ☎️Challenges and Objectives
  • METHOD
    • 📪RAG
    • 📎Finetuning (Phi-2)
    • 🧙‍♂️Response Scoring (Falcon7B)
    • 🤖Abbreviations
  • Final Systems
    • 🛞Phi-2
    • 🛻Falcon7B
  • REPRODUCING RESULTS
    • 🚀Installation Instructions
    • 🏓Phi-2
    • ⚾Falcon7B
  • Links
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  • 1. Insufficient LLM Knowledge
  • 2. Non-adherence to Instructions by LLMs
  • 3. Lexicon mismatch
  • Objectives
  1. Overview

Challenges and Objectives

An overview of the challenges we seek to address with our solution

1. Insufficient LLM Knowledge

Given the technical nature of the questions are and the little capacity of the LLMs to store knowledge in their weights, we find that the LLMs (Phi-2 and Falcon7B) lack the necessary knowledge to correctly answer the questions.

2. Non-adherence to Instructions by LLMs

We find that Phi-2 struggles to consistently provide answers in the desired format.

With Falcon7B, the responses seem to be uninformed from the question and context when options are provided in the prompt.

3. Lexicon mismatch

The questions seem to have many technical terms and abbreviations that are unknown to the LLM and are also not easy to infer from their use without prior understanding of the domain knowledge and terminology.

Naturally, our objectives are targeted towards addressing these challenges.

Objectives

  1. To develop a RAG pipeline that provides context that's relevant to the query

  2. To finetune Phi-2 for instruction alignment

  3. To develop an alternative means of prompting Falcon7B without the options

  4. To develop a glossary that helps to minimize the lexicon mismatch

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Last updated 10 months ago

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