Capabilities of LLM

📌 Table of Contents

• What Can and Can't LLMs Do?

• LLM Task Analysis

• LLM Business Use Cases

• LLM Limitations and Considerations

Introduction

Large Language Models (LLMs) are AI models that understand and generate natural language, producing text based on probabilistic predictions.

LLMs function through Next Token Prediction, enabling tasks like translation, summarization, and question-answering.

This module explores the tasks LLMs can and cannot perform in their business applications and essential considerations when using them.

1. What Can and Can't LLMs Do?

LLMs possess powerful language processing capabilities but are not flawless. Understanding what LLMs excel at and where they struggle is essential.

🙂 What LLMs Excel At

• Understanding and Generating Natural Language

• Code Generation and Debugging Assistance

🙁 Challenges LLMs Face

• Logical Reasoning and Problem Solving

• Maintaining Context Over Long Conversations

2. LLM Task Analysis

A deeper analysis of tasks LLMs handle effectively versus those they struggle with.

🙂 Tasks LLMs Perform Well

✔ General NLP (Natural Language Processing)

Task	Definition	Description
Text Generation	Generating new text content	Writing blog posts, ad copy, stories, and email drafts
Summarization	Summarizing documents concisely and effectively	Automatically summarizing news articles, research papers, and meeting minutes
Machine Translation	Translating from one language to another	Context-aware and natural multilingual translation
Question Answering, QA	Providing answers to questions	Retrieving information from documents & generating automatic responses
Text Classification	Classifying text into specific categories	Sentiment analysis (positive/negative) and email spam detection
Grammar Correction	Correcting grammar and spelling errors in text	Automatic detection and correction of grammar errors

✔ Code-Related Tasks

• Code Autocompletion (e.g., function generation)

• Code Debugging Support (e.g., error analysis and solution recommendations)

🙁 Tasks LLMs Struggle With

✖ Long-Form Context Tracking

• Difficulty maintaining consistent context over extended text or conversations.

✖ Advanced Logical Reasoning

• Logical puzzles, complex problem-solving, and mathematical reasoning are challenging.

✖ Processing Non-Textual Data

• LLMs cannot natively process images, audio, or tables without Multimodal AI integration.

3. LLM Business Use Cases

LLMs can be applied across various industries. Here are three primary use cases:

⚖️ 1) Legal: AI-Powered Legal Document Analysis

• Target Users: Legal Teams

• Problem: Legal teams deal with overwhelming amounts of legal documents, increasing workload and decreasing productivity.

• Solution: Upstage Solar LLM automates legal document analysis, accelerating case law research and document summarization, improving efficiency.

🏦 2) Finance: AI-Powered Financial Document Processing

• Target Users: Financial Institutions

• Problem: Handling hundreds of reports, meeting transcripts, and regulatory documents daily, with high risks of misinformation or reporting delays.

• Solution: LLM-powered AI automates report generation, meeting summary creation, and regulatory compliance searches, optimizing document processing workflows.

💊 3) Healthcare: Automating Discharge Summaries

• Target Users: Hospitals

• Problem: Physicians must compile patient histories, test results, and progress reports into discharge summaries, which increases their administrative workload.

• Solution: Upstage’s healthcare-specialized LLM automates the compilation of multiple medical documents into structured discharge summaries, reducing paperwork burdens.

Although LLMs have widespread applications, they are not omnipotent. Understanding their limitations is crucial.

4. LLM Limitations and Considerations

LLMs come with several challenges. Understanding these limitations and implementing appropriate mitigation strategies is essential.

⚠️ 1) Hallucination (False Information Generation)

LLMs sometimes generate information that does not exist or is factually incorrect. This phenomenon is called Hallucination and occurs because LLMs generate the most probable answer based on trained data, even if the response is fabricated.

📌 Characteristics:

• It can generate false but convincing information.

• May cite nonexistent sources or fabricated data.

• Can naturally present illogical or incorrect facts.

🔍 Examples:

• Fake Research Papers: "According to a 2023 MIT study..." (referring to a nonexistent paper)

• False Biographical Information: "Dr. X won the 2022 AI Nobel Prize..." (referencing an imaginary award or person)

• Historical Inaccuracy: "South Korea gained independence in 1800." (factually incorrect)

✅ Mitigation Strategies:

• Fact-Checking: Always verify LLM-generated content using reliable sources.

• Reference Data Linking: Connect external databases to LLMs for verifiable information. This methodology is called RAG(Retrieval-Augmented Generation).

• AI Feedback Loop: Utilize multiple AI models to cross-verify answers and improve reliability.

LLMs can produce plausible but inaccurate content—always verify their output.

⚠️ 2) Knowledge Cutoff (Limited Learning Scope)

LLMs only understand the information available up to their last training update and do not automatically learn new data.

📌 Characteristics:

• Cannot access or process post-training events, individuals, or innovations.

• Fails to incorporate the latest news, trends, or updates.

• Instead of stating "I don’t know," it may fabricate incorrect responses.

🔍 Examples:

• New Laws & Regulations: "Tell me about the 2024 tax reform." (The model may lack updated tax laws.)

• Product Releases: "What are the features of iPhone 15?" (If trained before iPhone 15’s release, it cannot provide details.)

✅ Mitigation Strategies:

• Real-Time Data Access: Use web search or external databases for live updates.

• Frequent Model Updates: Periodic retraining with new data improves accuracy.

LLMs may not reflect the latest information—use real-time sources when necessary.

⚠️ 3) Privacy and Data Security Risks

Entering sensitive personal or corporate information into LLMs poses data security risks.

📌 Characteristics:

• LLMs may store and recall entered data, leading to potential data leaks.

• Sensitive data like financial, healthcare, or corporate records should not be entered into public LLMs.

🔍 Examples:

• Customer Data Input: "This customer's social security number is 123-45-6789. Check their account balance."

• Internal Document Processing: "Summarize this confidential company document for me."

✅ Mitigation Strategies:

• Data Anonymization: Ensure private details (e.g., names, SSNs) are removed before using LLMs.

• Enterprise Security Protocols: Implement internal security measures for AI usage.

• Secure AI Solutions: Use private LLMs to prevent data leaks.

Never input sensitive information into public AI models. 🔒

Wrap Up

This article examined LLM capabilities, limitations, tasks, business applications, and essential considerations.

🔹 Tasks LLMs Excel At: Translation, summarization, code generation, conversational AI, and general NLP.

🔹 Tasks LLMs Struggle With: Long-form context retention, logical reasoning, and handling the latest information.

🔹 Business Applications: Used in finance, legal, healthcare, and manufacturing industries.

🔹 Considerations: Be cautious of Hallucination, Knowledge Cutoff, and Data Privacy Risks.

LLMs are potent tools but require a careful understanding of their limitations and appropriate mitigation strategies.

YoungHoon Jeon | AI Edu | Upstage