Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)

TL;DR

This paper reviews key challenges faced by large language models, such as domain specificity, knowledge forgetting, and bias, and discusses strategies for improving their reliability, fairness, and ethical standards.

Contribution

It provides a comprehensive overview of current challenges in LLM utilization and proposes future directions for addressing these issues through diversified training and ethical considerations.

Findings

LLMs struggle with domain-specific questions and knowledge retention.

Bias and superficial responses are common issues in LLM outputs.

Future trends include multimodal learning and real-time feedback mechanisms.

Abstract

With the development of large language models (LLMs) like the GPT series, their widespread use across various application scenarios presents a myriad of challenges. This review initially explores the issue of domain specificity, where LLMs may struggle to provide precise answers to specialized questions within niche fields. The problem of knowledge forgetting arises as these LLMs might find it hard to balance old and new information. The knowledge repetition phenomenon reveals that sometimes LLMs might deliver overly mechanized responses, lacking depth and originality. Furthermore, knowledge illusion describes situations where LLMs might provide answers that seem insightful but are actually superficial, while knowledge toxicity focuses on harmful or biased information outputs. These challenges underscore problems in the training data and algorithmic design of LLMs. To address these issues, it's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training. Future technological trends might lean towards iterative methodologies, multimodal learning, model personalization and customization, and real-time learning and feedback mechanisms. In conclusion, future LLMs should prioritize fairness, transparency, and ethics, ensuring they uphold high moral and ethical standards when serving humanity.