Knowledge Grounding for Language Models through Retrieval

Language Models (LMs) have shown remarkable capabilities in various applications due to their extensive pre-training. However, they often struggle when tasked with producing domain-specific or comprehensive responses, especially when it involves long-tail knowledge. To address this, we introduce a retrieval-augmented approach to enhance both small-scale and large-scale LMs, focusing on four principal challenges: hallucinated content, limitations in generating extensive, informative texts, domain adaptation, and the integration of structured knowledge into LMs. Our research is structured around four key research questions (RQs), each aimed at overcoming a specific limitation. RQ1 targets the reduction of hallucinated content in small-scale PLMs through the integration of retrieval mechanisms during end-to-end tuning. This is achieved by incorporating a memory network that infuses factual knowledge directly into the transformer architecture, enhancing factuality during the inference phase. RQ2 addresses the generation of lengthier and more informative texts by designing a pipeline-based framework to select pertinent knowledge that supports comprehensive answer generation. For large-scale LMs, RQ3 and RQ4 tackle the challenges of domain adaptation, as well as the efficient incorporation of heterogeneous knowledge. Our approach minimizes the need for extensive tuning by leveraging k-nearest neighbor retrieval techniques, allowing for a retriever's seamless integration with LLMs. This enables the models to access relevant information efficiently without requiring significant retraining. Additionally, a hybrid method that combines structured knowledge retrieval with prompt grounding is proposed to enhance the models’ ability to process complex queries and improve their performance in handling longtail knowledge. To evaluate the effectiveness of our proposed methodologies, we introduce a benchmark that measures how well these strategies enhance the precision and efficiency of LLMs in knowledge-rich environments. This approach not only promises to refine the utility of LLMs but also aligns with the critical needs of specialized applications, ensuring that LMs can operate effectively across a broader range of contexts.