Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders (2411.07870v5)

Abstract: Although people are impressed by the content generation skills of LLMs, the use of LLMs, such as ChatGPT, is limited by the domain grounding of the content. The correctness and groundedness of the generated content need to be based on a verified context, such as results from Retrieval-Augmented Generation (RAG). One important issue when adapting LLMs to a customized domain is that the generated responses are often incomplete, or the additions are not verified and may even be hallucinated. Prior studies on hallucination detection have focused on evaluation metrics, which are not easily adaptable to dynamic domains and can be vulnerable to attacks like jail-breaking. In this work, we propose 1) a post-processing algorithm that leverages knowledge triplets in RAG context to correct hallucinations and 2) a dual-decoder model that fuses RAG context to guide the generation process.

An Overview of "Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders"

The research paper titled "Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders" addresses the inherent challenges long associated with LLMs, particularly the complications arising from context-specific adaptability and hallucinations. Hallucinations refer to the generation of content not anchored in the verifiable source material, which poses significant reliability concerns, especially in domain-specific applications.

Core Contributions

The authors introduce two primary methodologies: a post-processing pipeline for rectifying hallucinations in LLMs, and an innovative dual-decoder architecture. These strategies aim to enhance the groundedness and factual accuracy of responses generated by LLMs.

1. Post-Processing Algorithm: The paper proposes a graph-based system to correct hallucinations in generated text. Knowledge triplets, representing entities as nodes and their relationships as edges, underpin this method. By cross-referencing these triplets with trusted domain knowledge extracted through Retrieval-Augmented Generation (RAG), the algorithm can eliminate or replace erroneous entities in generated outputs.
2. Dual-Decoder Model: A supplemental approach involves a dual-decoder model utilizing shared weights from a pre-trained LLM. By integrating context-augmented guidance, typically extracted from offline knowledge bases or RAG processes, it leverages cross-attention mechanisms. The approach emphasizes embedding guided context directly into the generative process, with the intent of enhancing the accuracy and completeness of responses to user prompts.

Experimental Results

The research implements these methodologies within a commercial context—adapting Microsoft product-related inquiries via LLMs. The paper employs several LLM variants, such as Phi-3.5 and LLama-3, to underscore performance differences when enhanced by proposed methods. Highlighted metrics include ROUGE-L, METEOR, domain-specific groundedness, and BERTScore, all demonstrating significant improvements with the TrustfulLLM components. The post-processing hallucination correction (HC) evidenced a notable boost in groundedness and coherency against baseline models.

Implications and Future Directions

The implications extend to improving the deployment of LLMs in business-critical environments where accuracy is paramount. The Trustful LLM framework's robustness is particularly valuable in domains where responses require impeccable grounding in dynamic, updated knowledge bases.

Future exploration could involve expanding these methodologies to heterogeneous multimodal systems, which could include not only textual but structured, visual, and temporal data. Moreover, investigating bias mitigation, federated learning integrations, and the efficient scaling of fine-tuning procedures across models remains a crucial path for further research. Additionally, advances in privacy-preserving techniques and agile response to rapid domain changes must be iteratively pursued.

Conclusion

This work significantly contributes to the iterative improvement and adaptation of LLMs for domain-specific applications. The novel dual-decoder architecture and robust post-processing contribute pathways toward addressing longstanding challenges in language generation models. By grounding content in verifiable knowledge structures and implementing fine-grained correction mechanisms, this research facilitates the crafting of more reliable and trustworthy AI-generated text.