ChatGPT hit 900 million weekly active users in January 2026, processing 2.5 billion prompts daily. LLMs now handle medical imaging analysis, weather forecasting, and code generation. But bias, inaccuracy, and hallucinations still limit adoption.
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See the future of large language models by delving into promising approaches, such as self-training, fact-checking, and sparse expertise that could LLM limitations.
Future trends of large language models
1- Real-Time Fact-Checking With Live Data
LLMs now access external sources during conversations instead of relying only on training data. Model queries external databases, retrieves current information, and provides citations.
Limitation: Still makes errors. Citations don’t guarantee accuracy; models sometimes cite sources incorrectly or misinterpret cited content.
- Microsoft Copilot: Integrates GPT-4 with live internet data. Answers questions based on current events with source links.
- ChatGPT: Searches the web when asked about recent events. Cites sources in responses.
- Perplexity: Built specifically for cited search. Every answer includes source links.
2- Synthetic training data
Models generate their own training datasets instead of requiring human-labeled data.
Google’s self-improving model (2023 research):
- Model creates questions
- Curates answers
- Fine-tunes itself on generated data
- Performance improved: 74.2% to 82.1% on GSM8K math problems, 78.2% to 83.0% on DROP reading comprehension
Figure: Overview of Google’s self-improving model
Source: “Large Language Models Can Self-Improve”
OpenAI, Anthropic, and Google are all using synthetic data to supplement human-labeled datasets. Reduces data labeling costs but introduces new bias risks; models can amplify their own mistakes.
3- Sparse Expert Models (Mixture of Experts)
Instead of activating entire neural network for every input, only relevant subset of parameters activates depending on task.
Model routes input to specialized “experts” within the network. Only activated experts process the query.
Real-life examples
- Llama 4 Scout: 109B total parameters, 17B active per token. Mixture of Experts (MoE) architecture delivers 10M token context window on single H100 GPU.
- Mistral DevStral: Purpose-built for coding with reinforcement learning. 128K token context optimized for coding workflows.
- DeepSeek V3: 671B total parameters, 37B activated per token using MoE. Trained on 14.8 trillion tokens focused on technical content.
4- Enterprise Workflow Integration
LLMs are embedded directly into business processes rather than used as standalone tools.
Real-life examples
Salesforce Einstein Copilot: Integrates LLMs into CRM operations. Answers customer queries, generates content, executes actions within Salesforce.
Microsoft 365 Copilot: Embedded in Word, Excel, PowerPoint, Outlook. Drafts documents, analyzes spreadsheets, generates presentations based on company data.
Anthropic Claude for Enterprise: Project-based memory separation. Startup roadmap stays separate from client work. Team-level memory sharing.
5- Hybrid LLMs with multimodal capabilities
Future advancements may include large multimodal models that integrate multiple forms of data, such as text, images, and audio, allowing these models to understand and generate content across different media types, further enhancing their capabilities and applications.
Example: OpenAI’s DALL·E, GPT-5, or Google’s Gemini provide multimodal capabilities to process images and text, enabling applications like image captioning or visual question answering.
6- Reasoning models
Models that “think” through problems step by step rather than generating immediate responses.
This shift from prediction to reasoning is critical for enabling:
- Agentic behavior, where models plan, execute, and adapt tasks autonomously.
- Interpretable AI, where outputs are step-by-step and logically sound, not just plausible-sounding.
Real-world example:
Claude Sonnet 4 with extended thinking: Hybrid reasoning approach, fast default mode, extended thinking mode for complex problems. Tool use during extended thinking. Can reason for hours if needed.2
7- Domain-Specific Fine-Tuned Models
Models trained on specialized data for specific industries instead of general-purpose training.
Google, Microsoft, and Meta are developing their own proprietary, customized models to provide their customers with a unique and personalized experience.
These specialized LLMs can result in fewer hallucinations and higher accuracy by leveraging:
- domain-specific pre-training
- model alignment
- supervised fine-tuning
See LLMs specialized for specific domains such as coding, finance, healthcare, and law:
- Real-life example:
- Coding: GitHub Copilot: Fine-tuned on code repositories. 85% of developers use AI coding tools by the end of 2025. Autocompletes code, generates functions, suggests bug fixes. 3
- Finance: BloombergGPT, a 50-billion-parameter LLM, is trained on finance-specific data.4
- Healthcare: Google’s Med-Palm 2 is trained on medical datasets.5
- Law: ChatLAW is an open-source language model specifically trained with datasets in the Chinese legal domain.6
- Domain-specific models reduce hallucinations and improve accuracy within their domain but perform worse outside it. BloombergGPT excels at financial analysis but is worse than GPT-4 at creative writing.
8- Ethical AI and bias mitigation
Companies are increasingly focusing on ethical AI and bias mitigation in the development and deployment of large language models (LLMs).
Real-life examples:
- Apple works with researchers to protect user data.
- Microsoft remains dedicated to ensuring safe AI practices. The company is engaging with researchers and academics to improve responsible AI practices.8
- Meta, IBM, and OpenAI are working on models that use Reinforcement Learning from Human Feedback (RLHF) to reduce bias and harmful outputs from models like GPT-4.
- Google’s DeepMind has an AI Ethics and Society team that focuses on mitigating biases in AI systems and improving fairness.9
Limitations of large language models (LLMs)
1- Hallucinations
Models generate plausible-sounding but incorrect information.
Figure: Hallucination benchmark for popular LLMs
Source: ResearchGate10
Best performers (2026):
- Claude Sonnet 4: Reduced hallucination through extended thinking mode
- GPT-5.2: Better uncertainty flagging
- Gemini 2.5 Pro: Improved citation accuracy
All models hallucinate. Frequency reduced but not eliminated. Critical applications still require human verification.
2- Bias
Models absorb and amplify social biases from training data.
Figure: Overall bias scores by models and size
Source: Arxiv11
Types of bias observed:
- Gender bias in occupation suggestions
- Racial bias in resume screening simulations
- Age bias in healthcare recommendations
- Socioeconomic bias in educational content
3- Toxicity
Models may generate harmful, offensive, or toxic content despite safety measures.
Figure: LLMs’ toxicity map
Source: UCLA, UC Berkeley Researchers12
*GPT-4-turbo-2024-04-09*, Llama-3-70b*, and Gemini-1.5-pro* are used as the moderator, thus the results could be biased on these 3 models.
Strict safety measures reduce toxicity but increase false positives (refusing harmless requests). Loose measures allow toxicity through.
4- Context Window Limitations
Every model has memory capacity limiting tokens it can process.
2026 context windows:
- Magic LTM-2-Mini: 100M tokens (~75M words, limited production evidence)
- GPT-5.2: 272,000 tokens (~200,000 words)
- Claude Sonnet 4: 1M tokens beta (~750,000 words)
- Gemini 2.5 Pro: 2M tokens (~1.5M words)
Figure: Word limit comparison between ChatGPT and GPT-4
Source: OpenAI
5- Static Knowledge Cutoff
Models rely on pre-trained knowledge with a specific cutoff date. Don’t have access to information after training unless connected to external sources.
Problems:
- Outdated information on current events
- Inability to handle recent developments
- Less relevance in dynamic domains (technology, finance, medicine)
Solution: Web search integration. ChatGPT, Claude, and Perplexity all offer real-time search. But search doesn’t eliminate hallucinations; models sometimes misinterpret search results.
Major LLM Platforms
GPT-5.2
Smart model routing: Simple queries → fast answers, complex → deep analysis
Multimodal: Process text and images. Generate code from screenshots, analyze documents, create alt text for accessibility.
Improvements over GPT-4:
- Reduced hallucination rate
- Better uncertainty flagging
- PhD-level reasoning depth
Who uses it: Developers, enterprises, content creators. Largest user base among LLMs.
Limitations: Still hallucinates. Expensive at scale. Knowledge cutoff means no real-time info without web search enabled.
Claude 4 Sonnet/Opus
Hybrid reasoning: Fast default mode, extended thinking mode for complex problems. Can “think” for hours if needed.
Memory implementation: Explicit activation only. Starts with a blank slate, activates memory when invoked through tool calls (conversation_search, recent_chats). Users see exactly when memory activates.
Project-based separation: Each project has a separate memory space. The startup roadmap stays separate from client work.
Extended thinking mode: Tool use during reasoning. Context awareness tracks its own token budget throughout conversations.
Who uses it: Developers preferring transparency, enterprises requiring control over memory/context, and teams managing multiple projects.
Limitations: Extended thinking mode slower and more expensive. 1M context beta availability limited to tier 4+ users.
Gemini 2.5 Pro
Multimodal processing: Native handling of text, audio, images, video. Can analyze full conversations including visual and audio context.
Code execution: Dynamic problem solving through code generation and execution.
Gemini 3.0 expected Q1 2026: Real-time 60fps video processing, multi-million token context windows, 3D object understanding, built-in reasoning by default (no manual toggle).
Who uses it: Google Cloud customers, developers building multimodal applications, and enterprises with complex document analysis needs.
Limitations: Response latency increases with very long contexts. Computationally intensive. Less mature API ecosystem than OpenAI.
Llama 4 Scout
Deployment: A single NVIDIA H100 GPU handles 10M token contexts. Native multimodality with an early fusion approach.
Who uses it: Researchers, organizations wanting open-source models, developers needing on-device deployment, companies avoiding vendor lock-in.
Limitations: Performance varies based on hosting configuration. Requires significant infrastructure investment for optimal performance. Less out-of-box polish than commercial models.
BLOOM
Largely superseded by newer open models (Llama 4, Mistral, DeepSeek). Remains available on Hugging Face for research and education.
Who still uses it: Researchers studying multilingual models, educational institutions, and developers in low-resource language communities.
Limitation: Training data from 2022. No updates to knowledge. Newer open models outperform it on most benchmarks.
For a comparative analysis of the current LLMs, check our large language models examples article.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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