This blog post discusses the limitations of Large Language Models (LLMs), such as knowledge cutoff, hallucinations, and lack of user customization. It explores two approaches to mitigate these issues: fine-tuning and retrieval-augmented generation. Fine-tuning involves supervised training with question-answer pairs to enhance the LLM’s performance, but it only postpones the knowledge cutoff problem and does not eliminate hallucinations. It is suggested for slowly changing datasets. In contrast, the retrieval-augmented generation uses LLMs as an interface to access external information, improving source citation, reducing hallucinations, and allowing for easier updates and personalization. However, it requires an effective search tool and access to a knowledge base. Future developments will be documented on Neo4j’s GitHub repository.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Integration of LLMs with Knowledge Graphs | Exploring the use of knowledge graphs to enhance LLM performance and fine-tuning. | Shift from solely using LLMs to incorporating knowledge graphs for better accuracy. | LLMs may evolve to seamlessly integrate with knowledge graphs, enhancing data retrieval and response accuracy. | The need for more accurate and contextually relevant responses in LLM applications. | 4 |
| Retrieval-Augmented Generation Trend | Growing trend of using retrieval-augmented methods to enhance LLM capabilities. | From relying solely on internal knowledge to utilizing external data sources for responses. | LLMs could become more efficient by primarily serving as interfaces for querying external knowledge bases. | Demand for real-time, accurate information in various applications. | 5 |
| Cost-effective Dataset Creation using LLMs | Using LLMs to generate training datasets for fine-tuning processes. | Shift from manual dataset creation to automated processes using LLMs for efficiency. | Training datasets might be predominantly generated by LLMs, reducing costs and time. | Need for scalable and cost-effective solutions in AI training. | 4 |
| Personalization in LLM Responses | Emerging focus on personalizing LLM outputs based on user context and access permissions. | Transition from generic responses to tailored answers based on user data. | LLMs may provide highly personalized interactions, improving user engagement and satisfaction. | Increased demand for personalized experiences in technology. | 5 |
| Knowledge Cutoff Mitigation Strategies | Developing techniques to handle knowledge cutoffs in LLMs, like fine-tuning. | From static knowledge bases to dynamic updates for LLMs. | LLMs might frequently update their knowledge bases, reducing obsolescence in information. | The fast-paced evolution of information and the need for up-to-date data. | 4 |
| Increased Awareness of LLM Limitations | Growing recognition of limitations like hallucinations and knowledge cutoffs in LLMs. | From uncritical acceptance of LLM outputs to a more skeptical and analytical approach. | Users and developers may develop stricter standards for LLM outputs, enhancing accountability. | The rising implications of misinformation and the need for reliable AI tools. | 5 |
| name | description | relevancy |
|---|---|---|
| Knowledge Cutoff Limitations | LLMs have a fixed knowledge cutoff, making them unaware of recent events or data, potentially leading to outdated information being provided. | 5 |
| Hallucinations of LLMs | LLMs may generate convincing but false information, which poses risks in situations requiring high accuracy. | 4 |
| Source Citation Issues | LLMs lack the ability to cite sources in their responses, making it difficult to verify the accuracy of the information provided. | 5 |
| Dependency on External Knowledge Sources | Retrieval-augmented generation relies on external databases, exposing applications to risks if those sources are inaccurate. | 4 |
| Biases in Training Data | Inherent biases present in training datasets can lead to skewed outputs from LLMs, impacting fairness and reliability. | 4 |
| Access Control and Data Privacy Concerns | LLMs currently do not implement access restrictions, risking unauthorized access to sensitive information. | 5 |
| Dependence on Intelligent Search Tools | The effectiveness of retrieval-augmented LLMs greatly relies on the quality of search tools, which can vary drastically. | 4 |
| Training Dataset Complexity | Creating effective training datasets for fine-tuning LLMs can be complex and costly, limiting accessibility for smaller teams. | 3 |
| Limitations in User Customization | Current LLMs lack personalization capabilities, which could limit user-specific responses and improve user experience. | 3 |
| name | description | relevancy |
|---|---|---|
| Community Engagement in LLM Development | Building open-source repositories for community learning and contributions on LLM applications and limitations. | 5 |
| Fine-Tuning for Specific Applications | Utilizing fine-tuning techniques to cater LLMs for specific tasks or to update their knowledge base. | 4 |
| Retrieval-Augmented Generation | Integrating LLMs with external data sources for real-time information retrieval instead of relying solely on internal knowledge. | 5 |
| Meta-Use of LLMs for Dataset Creation | Employing LLMs to generate training datasets for fine-tuning, showcasing a recursive use of AI technology. | 4 |
| Source-Citing Mechanisms | Implementing features that allow LLMs to cite sources for generated responses to enhance information validation. | 5 |
| Customization and Personalization of LLM Outputs | Adapting LLM responses based on user context and access permissions for more relevant interactions. | 4 |
| Addressing Hallucinations in LLMs | Developing strategies to mitigate the generation of false information by LLMs through various methods. | 5 |
| Integration of LLMs with Knowledge Graphs | Exploring the use of knowledge graphs to enrich the training datasets for LLMs and improve their performance. | 4 |
| name | description | relevancy |
|---|---|---|
| Large Language Models (LLMs) | Advanced AI models capable of understanding and generating human-like text, useful for various applications in natural language processing. | 5 |
| Fine-Tuning Techniques for LLMs | Methods to optimize LLMs’ performance by training them on specific question-answer pairs to enhance their capabilities. | 4 |
| Retrieval-Augmented Generation | An approach using LLMs to generate answers based on external documents, enhancing accuracy and reducing reliance on internal knowledge. | 5 |
| LangChain Library | A framework enabling LLMs to access real-time information from various sources, improving their functionality. | 4 |
| LlamaIndex (GPT Index) | A data framework that enhances LLM performance by allowing them to leverage private or custom data from diverse sources. | 4 |
| Knowledge Graphs for LLMs | Using structured knowledge graphs to create training datasets for LLM fine-tuning, improving their contextual understanding. | 3 |
| Plugins for LLMs | Extensions that allow LLMs to access up-to-date external information, enhancing their answer generation capabilities. | 4 |
| name | description | relevancy |
|---|---|---|
| Knowledge Cutoff in LLMs | The challenge of LLMs being unaware of events post-training, hindering their real-time application. | 5 |
| Hallucination in LLMs | LLMs generating plausible but incorrect information, complicating trust and verification of outputs. | 4 |
| Bias and Toxicity in Training Data | Concerns regarding the ethical implications of biases and toxic content present in LLM training datasets. | 4 |
| Retrieval-Augmented Generation | The trend of integrating LLMs with external data retrieval systems for more accurate and real-time responses. | 5 |
| Fine-Tuning Challenges | The complexity and cost associated with constructing effective fine-tuning datasets for LLMs. | 4 |
| User Customization Limitations | Lack of personalization and access controls in LLM responses, raising privacy and security issues. | 4 |
| Meta-Training Datasets | Using LLMs to create training datasets for themselves, raising questions about reliability and quality of data. | 3 |
| Integration of Knowledge Graphs | Exploring the use of knowledge graphs to enhance LLM capabilities and improve answer accuracy. | 4 |