Fine-Tuned LLMs & LLM Orchestration: Addressing Data Privacy & Mitigate Hallucination

In the age of data-driven enterprises, the need for sophisticated language models (LLMs) is ever more pressing. However, enterprises face a crucial decision: should they opt for commercial off-the-shelf LLM solutions or invest in building fine-tuned LLMs tailored to their specific needs? This whitepaper argues for the latter, elucidating why enterprises should consider fine-tuned LLMs to navigate challenges such as data privacy, protection, and the utilization of Response Generation (RAG) to mitigate hallucination. Furthermore, it proposes an LLM orchestration solution adept at handling query rephrasing, co-referencing, query type, item and attribution identification, response generation, and digression handling using RAG.

Introduction:

Language models have revolutionized the way enterprises interact with data, enabling more natural and intuitive communication. However, the one-size-fits-all approach of commercial LLMs may fall short when it comes to addressing the unique requirements and sensitivities of enterprise data environments. Fine-tuning LLMs offers a tailored solution to these challenges while leveraging cutting-edge techniques like RAG to ensure accurate and reliable responses.

Why Fine-Tuned LLMs?

1. Data Privacy and Protection: Enterprises are custodians of vast amounts of sensitive data, ranging from customer information to proprietary business insights. Off-the-shelf LLMs may not adequately address the intricacies of data privacy and protection protocols specific to each enterprise. Fine-tuning LLMs allows organizations to implement robust privacy measures, ensuring that sensitive information remains secure and confidential.
2. Customization and Adaptability: Enterprise data environments are diverse and dynamic, requiring LLMs to be adaptable to various contexts and domains. Fine-tuned LLMs offer the flexibility to tailor models according to specific industry verticals, regulatory requirements, and business objectives. This customization enhances the accuracy and relevance of LLM outputs, driving better decision-making and insights.
3. RAG to Mitigate Hallucination: Hallucination, the generation of seemingly plausible but factually incorrect responses, poses a significant challenge for LLMs. Retrieval augmented Generation (RAG) techniques provide a mechanism to mitigate hallucination by incorporating contextual information and fact-checking mechanisms into the response generation process. Fine-tuned LLMs can leverage RAG to ensure the integrity and reliability of generated responses, enhancing trust and credibility.

Building an LLM Orchestration Solution:

To address the complex requirements of enterprise data environments, we propose an LLM orchestration solution that encompasses the following key components:

1. Query Rephrasing and Co-Referencing: The solution incorporates advanced natural language processing techniques to rephrase queries and resolve co-references within the text. This ensures clarity and precision in understanding user intent and context, improving the accuracy of response generation.
2. Query Type, Item, and Attribution Identification: By classifying query types, identifying relevant items, and attributing sources, the orchestration solution enhances the depth and relevance of generated responses. This semantic understanding enables LLMs to provide contextually appropriate information, thereby enriching user interactions.
3. Response Generation with LLM: Leveraging LLMs for response generation techniques, the solution ensures that generated responses are factually accurate and contextually relevant. By integrating real-time fact-checking mechanisms and contextual validation, the solution mitigates the risk of hallucination, enhancing the reliability and trustworthiness of LLM outputs. Ability to manage the responses and edit the responses based on chucks identified.
4. Digression Handling: In dynamic conversational settings, digression handling is essential to maintain coherence and relevance for all the FAQs during any time in the conversation. The orchestration solution employs RAG-based techniques to identify and address digressions effectively, steering conversations back to the intended context while preserving user engagement and satisfaction.

Conclusion:

In conclusion, the imperative for enterprises to build fine-tuned LLMs tailored to their specific requirements cannot be overstated. By addressing data privacy, protection, and leveraging RAG techniques to mitigate hallucination, fine-tuned LLMs offer a compelling solution to the challenges facing modern enterprises. The proposed LLM orchestration solution provides a robust framework for handling query rephrasing, co-referencing, query type, item and attribution identification, response generation, and digression handling, empowering enterprises to harness the full potential of language models in driving innovation and competitiveness.

As organizations embark on their journey towards leveraging advanced language technologies, the adoption of fine-tuned LLMs represents a strategic imperative, enabling them to unlock new opportunities and insights in an increasingly data-centric world.

Learn more about Kore.ai Unified XO platform with Search Assist and bring your own enterprise small language models to our platform.