sophiatannir/TTP_LLMs_review

Challenges and Applications of Large Language Models

This repository contains a presentation on the challenges and applications of Large Language Models (LLMs) based on the paper by Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Robert Raileanu, and Robert McHardy. Read the full paper here: Challenges and Applications of Large Language Models

Table of Contents

• Introduction
• LLM Challenges
  • Design Challenges
  • Behavioral Challenges
  • Scientific Challenges
• Applications of LLMs
• Code Demo
• Critical Analysis
• Discussion and Q&A
• Citation

Introduction

The presentation discusses the rapid advancements in natural language processing (NLP) capabilities, such as GPT-3, PaLM, ChatGPT, and Gemini. The purpose of the paper is to identify open problems and review applications and challenges related to LLMs.

LLM Challenges

The paper groups challenges into three areas:

1. Design: decisions taken before deployment
2. Behavior: challenges that occur during deployment
3. Science: challenges that hinder academic progress

Design Challenges

• Unfathomable Datasets
• Tokenizer Reliance
• High Pre-Training Costs

Behavioral Challenges

• Prompt Brittleness
• Misaligned Behavior
• Outdated Knowledge

Scientific Challenges

• Brittle Evaluations
• Reliance on Static Human Evaluations
• Lacking Experimental Designs

Applications of LLMs

Chatbots

• Constraints:
  • Maintaining dialog coherence over multiple turns.
  • High latency in responses.

Computational Biology

• Main Focus: Protein sequence modeling and prediction.
• Challenges:
  • Transfer to downstream tasks like drug design remains difficult.

Computer Programming

• Applications:
  • Code generation, completion, and review.
• Key Constraint:
  • Incorporating long-range dependencies across code bases.

Creative Work

• Fields: Story, script, and text generation.
• Limitation:
  • Limited context window requires modular prompting strategies.

Knowledge Work

• Constraints:
  • Quantitative reasoning.
  • Risk of hallucinations.

Law

• Focus Areas:
  • Prediction, comprehension, and text generation.
• Central Challenge:
  • Outdated information.

Medicine

• Uses:
  • Question answering, information extraction, diagnosis, etc.
• Primary Constraints:
  • Hallucinations and biases.

Reasoning

• Assistance Areas:
  • Mathematical, common sense, and causal reasoning.
• Performance:
  • Remains below human levels on many tasks.

Robotics

• Applications:
  • Instruction following and planning.
• Limitation:
  • Reliance on single modalities limits capabilities.

Social Sciences

• Purpose:
  • Simulating human behavior and analyzing model psychology.
• Requirement:
  • Mitigating social biases.

Code Demo

A code demo is available in the repository: Code Demo

Critical Analysis

Overlooked Aspects

• Lack of Ethics Section or Mentioning:
  • The paper fails to include an ethics section or any mention of the ethical implications of the work. Given the importance of ethical considerations in research, this omission is a significant oversight.

Areas for Further Development

• Explaining Why Simplistic Solutions to Problems Aren’t Effective:
  • The paper could benefit from a more detailed explanation of why simplistic solutions are not viable for the problems discussed. This would help readers understand the complexity of the issues and the necessity for the proposed approaches.

Potential Errors

• While there are no errors in the paper itself, errors of omission could be considered for things like ethical considerations

Disputed Findings

• Have Others Disputed the Findings?:
  • There are no general disputes for this type of review - we would expect findings here to become outdated (especially challenges and their solutions) and the applications to rapidly expand.

Related Work Section

• Coverage of Similar and Further Developing Work:
  • The paper acknowledges work similar to its own but could develop further into ideas mentioned in the Related Work section. Expanding on how the current research builds upon or diverges from existing studies would provide a more comprehensive understanding of its contribution to the field.

Discussion and Q&A

Dataset Balancing and Bias Mitigation

• Question: Considering the large size of datasets used in training LLMs, what strategies could be implemented to ensure these datasets are balanced and free of biases without compromising the diversity and richness of the data?

Classification of Hallucinations

• Question: Based on this paper’s classification of problem areas (design, behavior, science), in what area would hallucinations be classified?

Challenges in LLM Development

• Possible Discussion Question: Which of the challenges is the furthest from being solved? Discuss reasons and potential solutions.

Growth Domains for LLM Applications

• Possible Discussion Question: What domain will grow the most with LLM applications? Consider current technological advancements and societal needs.

Challenges Across Domains

• Possible Discussion Question: What domain faces the most challenges with LLM applications? Discuss specific obstacles and how they might be overcome.

Additional Resources

• A Survey of Large Language Models
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• A Survey on Evaluation of Large Language Models
• ChatGPT for good? On opportunities and challenges of large language models for education
• HuggingFace

Citation

Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., & McHardy, R. (2023, July 19). Challenges and applications of large language models. arXiv.org. https://arxiv.org/abs/2307.10169