Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG (2410.02825v2)

Abstract: This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as doubled metrics compared to out-of-box LLM) in handling privacy-related queries, by grounding responses with factual information which reduces inaccuracies.

• The paper introduces a dual strategy that combines continual pre-training with retrieval-augmented generation to enhance domain understanding and mitigate hallucinations in LLMs.
• It leverages a comprehensive privacy knowledge base of 20,000 documents to reinforce the Llama-3.1 model’s familiarity with regulatory content.
• The semantic RAG layer improves factual accuracy by 24% over the pretrained baseline and 40% over the original model, ensuring contextually grounded responses.

Ingest-And-Ground: Addressing Hallucinations with RAG in Continually-Pretrained LLMs

The paper "Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG" explores a novel approach to enhance the accuracy and applicability of LLMs in privacy compliance assessments. The authors present a methodological framework that combines continual pre-training of a base LLM with retrieval-augmented generation (RAG) to address hallucinations, enhance domain knowledge, and provide fact-grounded responses.

Methodological Innovations

The authors implement a strategy that integrates continual pre-training of the Llama-3.1 model using a privacy-specific knowledge base, coupled with a semantic RAG layer. This dual approach seeks to fortify the LLM's capabilities in two primary ways: improving its domain-specific understanding and reducing the incidence of hallucinated content in its outputs. The implementation involves several key components:

1. Privacy Knowledge Base Development: The authors create a comprehensive database comprising approximately 20,000 documents, encapsulating a wide array of privacy laws, policies, and regulations. This dataset serves as both the foundation for additional pre-training of the LLM and as a source of contextual information in the RAG process.
2. Continual Pre-Training: The Llama-3.1 model undergoes further pre-training using the assembled knowledge base. This process involves standard techniques such as Causal Token Masking to adjust model weights, enhancing the model's familiarity with privacy-related content.
3. Semantic RAG Layer Implementation: To further reduce hallucinations, the authors utilize a state-of-the-art semantic RAG mechanism powered by the Dragon-Plus text embedding model. This layer dynamically retrieves relevant document segments based on an input query, ensuring that response generation is grounded in the most pertinent and up-to-date information.

Evaluation and Results

The paper presents an evaluation framework involving a benchmark of 50 privacy-related queries. The LLM's performance was assessed using a dual metric approach: pass rates determined by GPT-4 evaluations and keyword matching scores reflecting the presence of key response elements. The results showed substantial improvements:

• Continual pre-training alone increased content accuracy by 16%.
• The integration of the semantic RAG layer ameliorated hallucination issues, achieving a 24% improvement over the continually pre-trained model alone and a 40% enhancement compared to the original Llama model.

The authors conclude that the combination of domain knowledge reinforcement through continual pre-training and precise context anchoring via RAG significantly boosts the LLM's performance in providing accurate, contextually relevant responses.

Implications and Future Directions

This research holds significant implications for the application of LLMs in privacy compliance and beyond. By enhancing the factual grounding of LLMs, organizations can streamline privacy review processes, reducing the reliance on labor-intensive and time-consuming manual reviews. The methodologies presented serve as a compelling blueprint for addressing hallucinations, a ubiquitous challenge in AI text generation.

Future efforts could involve expanding the range and specificity of knowledge bases, optimizing retrieval processes, and customizing models to handle various privacy contexts and standards. More broadly, such systems could be adapted for other domains requiring high accuracy and up-to-date knowledge integration, facilitating enhanced decision-making, compliance verification, and knowledge sharing across complex regulatory landscapes.