Making AI Models Remember Better: The Challenge of Keeping Facts Straight

If you've ever chatted with ChatGPT or Claude and noticed they sometimes get basic facts wrong or contradict themselves, you're not imagining things. This is one of the biggest headaches in AI development right now, and it's harder to solve than you might think.

The Memory Problem That's Driving Engineers Crazy

Large language models like GPT-4 or Claude are basically pattern-matching machines on steroids. They've read millions of books, articles, and web pages during training, but here's the weird part – they don't actually "remember" facts the way humans do. Instead, they're incredibly good at predicting what word should come next based on patterns they've seen before.

This creates some bizarre situations. An AI might tell you that Paris is the capital of France in one sentence, then confidently state that London is France's capital two paragraphs later. It's not lying or trying to trick you – it genuinely doesn't have a consistent internal fact-checking system.

Why This Matters More Than Ever

As these models get integrated into search engines, educational tools, and business applications, getting facts right isn't just nice to have – it's essential. Nobody wants their AI assistant confidently telling them the wrong dosage for medication or giving incorrect historical dates for their research paper.

The stakes are particularly high in fields like:

• Healthcare and medical advice
• Financial planning and investment guidance
• Legal research and compliance
• Educational content for students
• News and journalism

The Technical Challenge Behind the Scenes

Here's what makes this problem so tricky to solve. Traditional databases store facts in neat, organized tables where you can easily look up "What is the capital of France?" But language models store information as weights and connections between billions of artificial neurons. There's no single place where the fact "Paris is the capital of France" lives – it's distributed across the entire network.

When the model generates text, it's not consulting a fact database. It's using statistical patterns to predict what sounds right based on its training. Sometimes those patterns align with factual accuracy, sometimes they don't.

Current Solutions and Their Trade-offs

Researchers are attacking this problem from several angles, each with its own pros and cons:

Retrieval-Augmented Generation (RAG) This approach connects the AI model to external databases or search engines. When asked a factual question, the model first looks up relevant information before generating its response. Companies like Microsoft and Google are heavily investing in this approach.

The upside? Much better factual accuracy for recent information. The downside? It's slower, more expensive, and doesn't help with the model's internal consistency.

Knowledge Editing Techniques Some teams are working on ways to directly modify the model's internal representations of facts. Think of it like performing surgery on the AI's "brain" to correct specific pieces of information.

This is promising but incredibly complex. Change one fact and you might accidentally mess up dozens of related concepts the model has learned.

Training on Curated Datasets Another approach focuses on being more selective about training data. Instead of feeding models everything on the internet (including plenty of misinformation), researchers are creating high-quality, fact-checked datasets.

The challenge here is scale. The internet has way more content than any human team could fact-check, but that messy, contradictory data is also what makes models so versatile.

What's Working in Practice

Some of the most promising real-world improvements come from hybrid approaches:

Multi-step Verification Instead of generating answers in one shot, newer systems break down complex questions into steps and verify each piece. This catches more inconsistencies before they reach the user.

Confidence Scoring Better models are getting trained to express uncertainty. When they're not sure about a fact, they'll say so rather than confidently stating something wrong.

Source Attribution Some systems now cite their sources, making it easier for users to verify information independently.

The Road Ahead

The honest truth? We're still in the early innings of solving this problem. Current AI models are amazing at many tasks, but they're not ready to replace encyclopedias or fact-checkers just yet.

The next few years will likely see significant improvements through:

• Better integration with real-time information sources
• More sophisticated internal fact-checking mechanisms
• Improved training methods that prioritize accuracy over creativity
• Hybrid systems that combine multiple approaches

What This Means for Users Right Now

While researchers work on these challenges, here's how to get the most accurate information from AI models today:

Ask for sources when possible. Many newer models can cite where their information comes from, making verification easier.

Cross-check important facts, especially for medical, legal, or financial advice. AI should supplement human expertise, not replace it.

Be specific in your questions. Vague queries often lead to vague, potentially inaccurate responses.

Pay attention to confidence levels. If a model seems uncertain or gives conflicting information, that's your cue to dig deeper.

The Bigger Picture

Improving factual consistency in AI isn't just a technical challenge – it's about building trust between humans and artificial intelligence. As these systems become more integrated into our daily lives, getting the details right becomes crucial for everything from education to decision-making.

The engineers and researchers working on this problem are tackling one of the fundamental challenges of artificial intelligence: how do you create a system that's both creative and accurate, flexible and reliable?

We're not there yet, but the progress over the past few years has been remarkable. The AI models of 2025 are significantly more factually consistent than those from just two years ago, and that trend shows no signs of slowing down.

The future of AI isn't just about making models smarter, it's about making them more trustworthy. And that's a goal worth working toward.

Attention-based models | Generative AI | Artificial Intelligence

Attention-based models help focus the network on important features of the input and ignore less important features.

Which LLM is more powerful for code generation

It is definitively difficult to say which LLM is the most powerful for generating the code as both capabilities and benchmarks are constantly evolving. However, here's a breakdown of some leading competitors to help you choose the right LLM:

1. Codex (OpenAI): This is a powerful LLM specifically designed for code. It can generate different programming languages, translate code, write unit tests, and more. It's closed-access, but limited access might be available through research collaborations.


2. Bard (Google AI): I am a large language model trained on a massive dataset of text and code. While I can't claim to be the absolute best, I can generate different programming languages, translate code, write basic comments and documentation, and follow your instructions to complete specific coding tasks.


3. Code Llama (Meta AI): This is a state-of-the-art LLM focused on code generation. It excels at generating code from natural language descriptions and code comments. It's open-source and readily available for experimentation.


4. Github Copilot (Microsoft/GitHub): This is a code completion tool powered by OpenAI's Codex. It integrates with various IDEs and suggests relevant code snippets as you type, improving development efficiency. While not a standalone LLM, it showcases Codex's capabilities in a practical application.

Choosing the Right LLM:

The best LLM for you depends on your specific needs:

Openness: If open-source availability is a priority, Code Llama might be a good choice.

Task focus: For tasks like code completion and translation, Github Copilot or Codex could be strong options.

Research and experimentation: If you're exploring cutting-edge capabilities, Codex or Bard might be worth investigating (considering access limitations for Codex).

Experiment with multiple LLMs: 

If possible, try out different LLMs for your specific coding tasks. Each LLM may have strengths and weaknesses depending on the context.

Remember, the field of LLM code generation is rapidly evolving. New models and advancements are constantly emerging. Stay updated on the latest developments to find the best tool for your coding needs.

Llama2 models

Meta Llama 2 models

Image source: DeepLearning.AI

LLM response of question "Who is your father?"

I'm just an AI, I don't have personal experiences or a physical body, so I don't have a father or a mother. I exist solely as a digital entity, designed to assist and provide information to users like you. My purpose is to help users like you with their questions and tasks, and to provide information on a wide range of topics.

Does OpenAI or ChatGPT support discrimination and its models is based on biased data?

OpenAI is committed to addressing bias and avoiding the amplification of unjust practices. However, models like ChatGPT are trained on large datasets from the internet, which may contain biases. OpenAI is actively working on research and engineering to reduce both glaring and subtle biases in how ChatGPT responds to different inputs. They are also providing clearer instructions to reviewers about potential pitfalls tied to bias and controversial themes. The aim is to improve default behavior, allow customization within broad bounds, and involve the public in decisions about the system's rules. OpenAI is dedicated to learning from mistakes and iterating on their models and systems to make them better over time.

What is prompt engineering and what are the principles of prompt engineering?

Prompt engineering is a critical component in the development of effective AI models, particularly in the context of natural language understanding (NLU) and natural language generation (NLG). It involves crafting prompts, questions, or queries that are presented to AI models to instruct them on how to respond to user inputs. The goal of prompt engineering is to create high-quality prompts that yield accurate, relevant, and unbiased responses from AI models. Here are the key principles of prompt engineering:

Image generated with Adobe Firefly

1. Clarity and Specificity: Prompts should be clear, concise, and specific. They must convey the user's intent without ambiguity. Vague prompts can lead to incorrect or irrelevant responses.
   
   
2. Relevance: Ensure that prompts are directly relevant to the task or query at hand. Irrelevant prompts can confuse the AI model and result in poor responses.
   
   
3. Diversity: Use a diverse set of prompts to train the AI model. A range of prompts helps the model understand different phrasings and variations in user queries.
   
   
4. User-Centric Language: Craft prompts that mirror how users naturally communicate. Use language and phrasing that align with your target user group.
   
   
5. Bias Mitigation: Be vigilant about potential bias in prompts. Biased or sensitive language can lead to discriminatory or harmful responses. Prompts should be free from any form of bias.
   
   
6. Testing and Iteration: Continuously test and refine prompts through user feedback and performance evaluation. Regular iteration is crucial for improving the model's performance.
   
   
7. Data Quality: High-quality training data is essential. Ensure that prompts used during model training are derived from reliable and diverse sources. The quality of data directly impacts model accuracy.
   
   
8. Variety of Inputs: Include prompts that cover a wide range of possible inputs. This prepares the model to handle a broader spectrum of user queries effectively.
   
   
9. Ethical Considerations: Prompts should adhere to ethical guidelines, respecting privacy and avoiding any harmful, offensive, or misleading content.
   
   
10. Transparency: Prompts should be transparent to users, meaning users should have a clear understanding of the AI's capabilities and limitations. Avoid obfuscating the fact that a user is interacting with an AI.
    
    
11. Context Awareness: Ensure prompts account for context and maintain a coherent conversation with the user. Contextual prompts enable more meaningful interactions.
    
    
12. Multimodal Inputs: In addition to text prompts, consider incorporating other forms of input such as images or voice to make interactions more interactive and user-friendly.
    
    

Effective prompt engineering is pivotal for the success of AI systems, as it shapes how the AI model interprets and responds to user queries. By following these principles, developers and engineers can create prompts that lead to more accurate and reliable AI interactions.

Large Language Models Evolution

The evolution of Large Language Models (LLMs) has been a significant development in the field of natural language processing (NLP) and artificial intelligence (AI). Here is a simplified overview of the evolution of LLMs:

LLM Evolution, Source: arxiv.org/abs/2304.13712v2

1. Early NLP Systems (Pre-2010): Before the era of LLMs, NLP systems relied on rule-based approaches and statistical models. These systems had limited capabilities and struggled with understanding context and generating human-like text.

2. Introduction of Neural Networks (2010s): The breakthrough came with the resurgence of neural networks and deep learning in the early 2010s. This led to the development of more sophisticated NLP models.

3. Rise of Word Embeddings (2013): Word embeddings, like Word2Vec and GloVe, were introduced. These models could represent words in dense vector spaces, capturing semantic relationships between words.

4. Sequence-to-Sequence Models (2014): Models like Sequence-to-Sequence (Seq2Seq) and Long Short-Term Memory (LSTM) networks improved tasks like machine translation and text summarization. However, these were still not true LLMs.

5. GPT-1 (2018): The release of "Generative Pre-trained Transformer 1" (GPT-1) by OpenAI marked a significant milestone. GPT-1 was pre-trained on a massive amount of text data and could generate coherent and contextually relevant text. It had 117 million parameters.

6. BERT (2018): Google introduced BERT (Bidirectional Encoder Representations from Transformers), which achieved state-of-the-art results on various NLP tasks. BERT improved contextual understanding by considering both left and right context.

7. GPT-2 (2019): OpenAI released GPT-2, a larger and more capable version of its predecessor. It had 1.5 billion parameters but was initially considered "too dangerous" to release at full scale due to concerns about its potential misuse.

8. GPT-3 (2020): GPT-3, with 175 billion parameters, is one of the largest LLMs to date. It demonstrated remarkable capabilities in natural language understanding and generation, powering a wide range of applications, from chatbots to content generation.

9. Specialized Models: Beyond GPT-3, specialized LLMs emerged, such as T5 (Text-To-Text Transfer Transformer), RoBERTa, and XLNet, each fine-tuned for specific NLP tasks.

10. Ethical and Societal Concerns: The rapid development of LLMs raised concerns about ethical use, bias in AI, and the potential to spread misinformation.

11. Continued Research: Research in LLMs continues to evolve, focusing on improving efficiency, reducing biases, and addressing ethical concerns.

12. Future Trends: The future of LLMs includes even larger models, more fine-tuning, addressing biases, and ensuring responsible AI development.

The evolution of LLMs has revolutionized the field of NLP, enabling more accurate and context-aware natural language understanding and generation. However, it also brings challenges that need to be carefully managed to ensure responsible and ethical use.

Should I invest in the Large Language Model industry in 2023?

Large Language Models (LLMs) are AI systems capable of processing and understanding natural language at an advanced level. They have gained popularity in recent years due to their ability to perform a variety of tasks, from language translation to natural language processing, and even creative writing. With the growing demand for LLMs, the question arises, is 2023 a good year to invest in the LLM industry?

Industry Trends

According to the Forbes Advisor report, the technology sector will continue to dominate the investment industry in 2023. Advancements in AI technology, coupled with increased funding for research and development, will drive the growth of the industry in the coming years. The report also highlights that the LLM industry is expected to grow at an average annual rate of 23.5% from 2021 to 2028. This growth is fueled by the increasing demand for natural language processing and sentiments analysis in fields such as customer service, digital marketing, and healthcare.

Potential Risks

However, investment in the LLM industry is not without risk. The World Economic Forum’s Global Risks Report 2023 identifies environmental risks as the most significant risks facing the world over the next ten years. While the LLM industry does not contribute directly to environmental risk, the reliance on fossil fuels and energy-intensive hardware for training and running LLMs can have a significant impact on the environment. Therefore, investing in LLM companies that prioritize environmental sustainability, such as through the use of renewable energy sources, can mitigate investment risk over the long term.

The report also highlights that the cost-of-living crisis is seen as the most significant risk in the next two years. In the current economic climate, investors will need to be cautious when investing in the LLM industry, as it is a relatively new and rapidly evolving industry. The risk of investing in a company that is not well-established or lacks a solid track record can result in significant losses. Therefore, thorough research, analysis, and caution are essential while making investment decisions in the LLM industry.

Market Potential

Despite the potential risks, the LLM industry’s market potential is growing rapidly, providing an attractive investment opportunity for investors. The LLM market size is expected to reach $37.8 billion by 2026, which represents an approximately six-fold increase from the market size in 2020. This growth is attributed to the increasing demand for advanced AI technologies that can handle complex natural language comprehension tasks. The rise of chatbots, virtual assistants, and smart speakers in the field of customer service and communication has also contributed to the growth of the LLM industry.

Investment Opportunities

Several companies offer promising investment opportunities in the LLM industry. OpenAI, one of the leading companies in the industry, has developed the GPT-3 LLM, which can create text that is almost indistinguishable from that written by humans. The company has raised significant funding from investors such as Microsoft and is considered a pioneer in the LLM industry. Another promising company is Google’s BERT LLM, which is being used extensively in natural language processing tasks in various fields, including healthcare, finance, and e-commerce.

In addition to these large companies, some promising start-ups in the LLM industry are also worth considering for investment opportunities. Hugging Face, a startup that specializes in building open-source conversational AI models and providing related services, has shown impressive growth in recent years. The company has raised $15 million in funding and has several high-profile clients, including Microsoft and PayPal.

Conclusion

Investors seeking to invest in the LLM industry in 2023 should be aware of the potential risks and opportunities associated with this rapidly growing industry. While the industry shows promising growth potential, investors need to be cautious when making investment decisions due to the industry’s relative infancy and potential environmental impact. Investing in well-established companies with a proven track record, such as OpenAI and Google, or promising start-ups such as Hugging Face, could lead to significant returns for investors.

Investing in the LLM industry requires thorough research, analysis, and caution, and investors should diversify their portfolio to minimize risk. Overall, the LLM industry’s growth potential and the increasing demand for advanced AI technologies that can handle complex natural language comprehension tasks make it a viable investment opportunity for investors willing to accept some level of risk.

References

• Forbes Advisor. (2023). Top Investing Trends For 2023. Retrieved from https://www.forbes.com/advisor/investing/top-investing-trends-2023/
• World Economic Forum. (2023). Global Risks Report 2023. Retrieved from https://www.weforum.org/agenda/2023/01/these-are-the-biggest-risks-facing-the-world-global-risks-2023/
• World Economic Forum. (2023). Global Risks Report 2023: We know what the risks are - here’s what … Retrieved from https://www.weforum.org/agenda/2023/01/global-risks-report-2023-experts-davos2023/

History of Large Language Models?

Large Language Models (LLMs) have emerged as one of the most transformative breakthroughs in the field of Artificial Intelligence (AI) and Natural Language Processing (NLP). These models have revolutionized the way machines process and generate human language, opening up new possibilities for communication, automation, and human-machine interaction.

The journey of LLMs traces back to the early days of AI research when linguists and computer scientists began exploring ways to enable machines to understand and generate human language. The 1950s and 1960s saw the development of early language processing systems, but it wasn't until the 1980s that researchers made significant strides in the domain of NLP.

In the late 1980s and early 1990s, statistical models like Hidden Markov Models and n-grams gained popularity in language processing tasks, such as speech recognition and machine translation. However, these models had limitations in handling complex language structures and lacked the ability to understand contextual nuances.

Large Language Model after 2018

The turning point for LLMs came in 2013 with the introduction of Word2Vec, a neural network-based model developed by Tomas Mikolov and his team at Google. Word2Vec used a technique called word embeddings to represent words in a continuous vector space, capturing semantic relationships and contextual information. This breakthrough paved the way for more sophisticated language models that could understand relationships between words and their context.

In 2018, OpenAI released the GPT (Generative Pre-trained Transformer) model, designed to predict the next word in a sentence using the transformer architecture. GPT marked a significant step forward in LLMs, utilizing a large neural network with multiple layers and self-attention mechanisms. This allowed the model to understand the context of a sentence and generate coherent and contextually relevant responses.

The real breakthrough, however, came with the release of GPT-3 in 2020 by OpenAI. GPT-3 is one of the largest language models to date, with a staggering 175 billion parameters. Its massive size enabled it to perform a wide range of language tasks, from translation and summarization to coding and conversation, all with remarkable accuracy.

GPT-3's capabilities have sparked excitement and debate about the potential applications and ethical implications of such powerful AI language models. While it has demonstrated impressive language understanding and generation, questions regarding bias, data privacy, and responsible use of AI have also been raised.

Beyond GPT-3, the race to build even larger and more capable language models continues. Several organizations and research teams are investing heavily in developing and fine-tuning their models to tackle increasingly complex language tasks. These models are likely to have profound implications for various industries, including healthcare, customer service, education, and content creation.

However, as LLMs become more pervasive, there is a growing emphasis on ethical considerations and transparency. Ensuring that these models are developed responsibly, with adequate safeguards against misuse, is a critical challenge for the AI community.

In conclusion, the history of Large Language Models is a testament to the relentless pursuit of advancing AI capabilities in understanding and processing human language. From humble beginnings with statistical models to the massive neural networks of today, LLMs have significantly transformed the landscape of AI and NLP. As researchers and developers push the boundaries further, the responsible development and deployment of these powerful models become paramount for a future where AI augments human potential while addressing societal needs and concerns.

What are the some large language models, and when and where those get developed?

As of my last update in September 2021, there were several large language models developed by different organizations. Here are some prominent examples and their development timelines:

1. GPT (Generative Pre-trained Transformer)

   - Developed by: OpenAI

   - Development Timeline: Introduced in 2018

   - Description: GPT was one of the first large-scale language models to use the transformer architecture and pre-training techniques to generate human-like text. It laid the foundation for subsequent models like GPT-2 and GPT-3.

2. GPT-2 (Generative Pre-trained Transformer 2)

   - Developed by: OpenAI

   - Development Timeline: Released in February 2019

   - Description: GPT-2 is an advanced version of the original GPT model with 1.5 billion parameters, making it even more powerful in generating coherent and contextually relevant text.

3. GPT-3 (Generative Pre-trained Transformer 3)

   - Developed by: OpenAI

   - Development Timeline: Introduced in June 2020

   - Description: GPT-3 is one of the largest language models to date, with a staggering 175 billion parameters. Its massive size enables it to perform a wide range of language tasks with impressive accuracy, from translation and summarization to code generation and conversation.

4. BERT (Bidirectional Encoder Representations from Transformers)

   - Developed by: Google AI Language

   - Development Timeline: Introduced in October 2018

   - Description: BERT is a transformer-based model that uses bidirectional attention to better understand the context of words in a sentence. It significantly improved the performance of various NLP tasks, including sentiment analysis, question answering, and named entity recognition.

5. XLNet

   - Developed by: Google Brain and Carnegie Mellon University

   - Development Timeline: Released in June 2019

   - Description: XLNet is another transformer-based language model that combines the ideas of autoregressive and bidirectional pre-training. It achieved state-of-the-art results on multiple NLP benchmarks.

6. RoBERTa (A Robustly Optimized BERT Pretraining Approach)

   - Developed by: Facebook AI Research (FAIR)

   - Development Timeline: Released in October 2019

   - Description: RoBERTa is a variant of BERT that optimizes the pre-training process, leading to improved performance on a wide range of NLP tasks.

7. T5 (Text-to-Text Transfer Transformer)

   - Developed by: Google Research Brain Team

   - Development Timeline: Introduced in January 2020

   - Description: T5 is a text-to-text transformer that frames all NLP tasks as a text-to-text problem. It showed promising results in transfer learning and few-shot learning settings.

Please note that the field of NLP and AI is rapidly evolving, and new language models may have been developed or updated since my last update. For the most current information, I recommend referring to official publications and announcements from the respective research organizations.

References

1. "Improving Language Understanding by Generative Pre-Training" by Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. (2018)

2. "Language Models are Unsupervised Multitask Learners" by Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. (2019)

3. "Language Models are Few-Shot Learners" by Tom B. Brown, Benjamin Mann, Nick Ryder, and et al. (2020)

4. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. (2019)

5. "XLNet: Generalized Autoregressive Pretraining for Language Understanding" by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. (2019)

6. "RoBERTa: A Robustly Optimized BERT Pretraining Approach" by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. (2019)

7. "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. (2020)