The Future of Large Language Models in 2025

Interest in large language models (LLMs) is rising since ChatGPT attracted over 200 million monthly visitors in 2024.¹ LLMs along with generative AI have an influence on a variety of areas, including medical imaging analysis and high-resolution weather forecasting.

However, their effectiveness is hindered by concerns surrounding bias, inaccuracy, and toxicity, which limit their broader adoption and raise ethical concerns.

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- Fact-checking with real-time data integration

LLMs will focus on conducting fact-checks based on real-world implementation by:

• Accessing external sources
• Providing citations and references for answers

This will allow LLMs to offer up-to-date information rather than relying solely on pre-trained static datasets.

Real-life example: Real-time AI assistants Microsoft Copilot (formerly called Bing Chat) integrate GPT-4 with live internet data to answer questions based on current events.²

Although it is still early to conclude that accuracy, fact-checking, and static knowledge base problems can be overcome in the near-future models, current research results are promising for the future.

This may reduce the need for using prompt engineering to cross-check model output since the model will already have cross-checked its results.

2- Synthetic training data

Researchers are working on large language models that can generate their own training data sets (i.e. generating synthetic training data sets). 

Google researchers developed a large language model capable of creating questions and fine-tuning itself using the curated answers. The model’s performance improved from 74.2% to 82.1% on GSM8K and from 78.2% to 83.0% on DROP.

Figure: Overview of Google’s self-improving model

Source: “Large Language Models Can Self-Improve”

3- Sparse expertise

Large Language Models (LLMs) will increasingly leverage sparse expert models.

Sparse models will allow certain parts of the model to specialize in specific tasks or knowledge. Instead of activating the entire neural network for every input (e.g. only a relevant subset of parameters depending on the task or prompt.)

This will allow LLM models to make sense of the neural activity within language models by focusing only on the most necessary parts.

Real-life example: OpenAI is exploring sparse models to make sense of neural networks and improve LLMs’ scaling and specialization.³

Future iterations may include sparse activation to optimize resource usage, potentially leading to more efficient, task-specific models without the computational intensity of fully dense networks.

4- LLMs integration into enterprise workflows

LLMs will be deeply integrated into business processes such as customer service, human resources, and decision-making tools.

Real-life example: Salesforce Einstein Copilot is an enterprise-wide customer service AI that integrates LLMs to enhance service/retail, sales, marketing, and CRM operations,  by answering queries, generating content, and carrying out actions.

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-4, or Google’s Gemini provide multimodal capabilities to process images and text, enabling applications like image captioning or visual question answering.

6- Reasoning models

Reasoning models represent the next stage in the evolution of large language models. They empower LLMs to move from surface-level fluency to deep cognitive function across complex tasks (e.g., scientific research, or strategic decision-making).

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:

Developers use Anthropic’s Claude 3.7 Sonnet, a reasoning model, to refactor code.⁴

7- Fine-tuned domain-specific LLMs

Gartner Poll finds that 70% of firms are investing in generative AI research to incorporate it into their business strategies.⁵

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 is fine-tuned to assist with coding tasks.⁶
  • Finance: BloombergGPT, a 50-billion parameters LLM, is trained on finance-specific data.⁷
  • Healthcare: Google’s Med-Palm 2 is trained on medical datasets.⁸
  • Law: ChatLAW is an open-source language model specifically trained with datasets in the Chinese legal domain.⁹

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.
  • To illustrate its commitment to AI ethics, the tech giant joined a study group called the Partnership on AI.¹⁰
• Microsoft remains dedicated to ensuring safe AI practices. The company is engaging with researchers and academics to improve responsible AI practices.¹¹
• 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.¹²

What is the current stage of large language models?

Scaling of models: The newest LLMs, like GPT-4 (1.8T parameters), Claude 3 (2T parameters), and Meta’s LLaMA 3 (405B parameters), are being trained on billions (or trillions) of parameters, further improving capabilities in natural language understanding, code generation, and reasoning.

Benchmarks – AI is improving: These models are performing at or near human-level accuracy on reading, image recognition, etc.

Source: ContextualAI¹³

Task specialization and fine-tuning: LLMs are now being fine-tuned for specific domains, such as healthcare (e.g., Med-PaLM 2), law, and science. Models like Radiology-Llama2 and MedAlpaca are fine-tuned with domain-specific data, allowing for more accurate and context-relevant outputs in specialized fields.

Read more: Large Language Models in Healthcare.

Integration beyond text: LLMs are advancing toward multi-modal capabilities, where they can process not only text but also images, audio, and even video. OpenAI’s GPT-4 and Google’s Gemini models are examples of multi-modal models that can interpret text alongside other media formats.

Safety mechanisms – adopting ethics: Leading LLMs are now designed with improved safety protocols to minimize biased outputs. For instance, Anthropic’s Claude models have integrated ethical AI design principles to ensure safer language generation.¹⁴

Limitations of large language models (LLMs)

1- Accuracy

Accuracy benchmarks often measure LLMs’ ability to perform tasks such as fact-checking or answering questions from structured data. Models like GPT-4, and OpenAI-o1-mini show improved accuracy.

Figure: Hallucination benchmark for popular LLMs

Source: ResearchGate¹⁵

2- Bias

Large language models facilitate human-like communication through speech and text. However, recent findings indicate that more advanced and sizable systems tend to assimilate social biases present in their training data, resulting in sexist, racist, or ableist tendencies.

Figure:  Overall bias scores by models and size

Source: Arxiv¹⁶

3- Toxicity

LLMs may generate toxic, harmful, or offensive content due to inherent biases or failure to identify harmful language.

Figure: LLMs’ toxicity map

Source: UCLA, UC Berkeley Researchers¹⁷

*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.

4- Capacity limitations

Every large language model has a specific memory capacity, which restricts the number of tokens it can process as input. For example, ChatGPT has a 2048-token limit (approximately 1500 words), preventing it from comprehending and producing outputs for inputs that surpass this token threshold.

GPT-4 extended the capacity to 25,000 words, far exceeding the ChatGPT model depending on GPT-3.5, allowing room for better performance.

Figure: Word limit comparison between ChatGPT and GPT-4

Source: OpenAI

5- Pre-trained knowledge set

LLMs like GPT-4 rely on pre-trained knowledge sets, meaning they are trained on large-scale datasets and retain information from that period up until a specific point (the “knowledge cutoff”).

This creates limitations because they do not have access to real-time data or updates unless fine-tuned later or connected to external sources.

This leads to several problems such as:

• Outdated or incorrect information
• Inability to handle recent events
• Less relevance in dynamic domains like technology, finance, or medicine

What are the popular large language models?

Gemini (Google)

Gemini is Google’s, launched in 2023, is created by Google’s AI research teams DeepMind and Google Research. It comes in four tiers:

• Gemini Ultra is the highest-performing Gemini model.
• Gemini Pro is a lightweight alternative to Ultra.
• Gemini Flash is a faster, “distilled” version of Pro.
• Gemini Nano is the free tier for image analysis, speech transcription, and text generation.

All Gemini models are multimodal, and Google claims that they were pre-trained and fine-tuned on 1T parameters based on proprietary audio, images, and videos, a large set of codebases, and text in different languages.

This distinguishes Gemini from models like Google’s own LaMDA, which was trained solely on text.

GPT-4 (OpenAI)

The largest language model is now OpenAI’s GPT-4, released in March 2023. Although the model is more complex than the others in terms of its size, OpenAI didn’t share the technical details of the model.

GPT-4 is a multimodal large language model of significant size that can handle inputs of both images and text and provide outputs of text, some applications include:

• Writing: Create a text output in your preferred tone of voice (e.g., creative, professional).
• Code extraction from the image: Receive the HTLML & CSS code based on the webpage image input.
• Drafting: Submit a photo and request that GPT-4 provide informative alt text.

OpenAI claims that:

• GPT-4 can handle approximately 25,000 words of text, allowing for use cases like long-form content development, and complex chats.
• GPT-4 is ~80% less likely to reply to requests for restricted content and 40% more likely to produce accurate responses than GPT-3.5.¹⁸

For a more detailed account of these capabilities of GPT-4, check our in-depth guide.

Claude 3 (Anthropic)

Claude 3 is Anthropic’s third-generation AI transformer model, designed to offer advanced natural language processing capabilities.

Claude is claimed to be able to analyze 100,000 tokens of text, equivalent to nearly 75,000 words in a minute—up from 9,000 tokens when it was first released in March 2023.¹⁹

Users can integrate Claude 3 into their virtual assistant platforms for task automation and customer interaction management, For example, Salesforce enables users to integrate Claude in their APIs.²⁰

It is available in three distinct tiers—Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku—each tailored to different use cases, from large-scale language generation to more concise, specialized tasks.

BLOOM (BigScience)

BLOOM, a 176B-parameter open-access language model released in 2022, is trained to comprise hundreds of sources in 46 natural and 13 programming languages.

BLOOM is open source, researchers can now download, run, and study the model on Hugging Face.

For a comparative analysis of the current LLMs, check our large language models examples article.

External Links

• 1. chat.openai.com Traffic Analytics, Ranking & Audience [April 2025] | Similarweb.
• 2. Bing Search now in Chat + when will it be - API - OpenAI Developer Community.
• 3. Extracting Concepts from GPT-4 | OpenAI.
• 4. Build Anything with Claude 3.7, Here’s How - YouTube.
• 5. Gartner Poll Finds 45% of Executives Say ChatGPT Has Prompted an Increase in AI Investment.
• 6. GitHub Copilot · Your AI pair programmer · GitHub.
• 7. Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance | Press | Bloomberg LP.
• 8. Sharing Google’s Med-PaLM 2 medical large language model, or LLM | Google Cloud Blog. Google Cloud
• 9. [2306.16092] Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model.
• 10. Transparency and Governance - Partnership on AI.
• 11. 502 Bad Gateway.
• 12. The ethics of advanced AI assistants - Google DeepMind.
• 13. Plotting Progress in AI - Contextual AI. Contextual AI
• 14. AI Governance and Accountability: An Analysis of Anthropic’s Claude.
• 15. GitHub - vectara/hallucination-leaderboard: Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents.
• 16. “Benchmarking Cognitive Biases in Large Language Models as Evaluators” arxiv. 2024. Accessed September 12, 2024.
• 17. OR-Bench: An Over-Refusal Benchmark for Large Language Models.
• 18. GPT-4 | OpenAI.
• 19. Introducing Claude \ Anthropic.
• 20. Supported Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Models | Mo.