Zero-shot Slot Filling with DPR and RAG (2104.08610v1)

Abstract: The ability to automatically extract Knowledge Graphs (KG) from a given collection of documents is a long-standing problem in Artificial Intelligence. One way to assess this capability is through the task of slot filling. Given an entity query in form of [Entity, Slot, ?], a system is asked to `fill' the slot by generating or extracting the missing value from a relevant passage or passages. This capability is crucial to create systems for automatic knowledge base population, which is becoming in ever-increasing demand, especially in enterprise applications. Recently, there has been a promising direction in evaluating LLMs in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent. The recent advancements in the field try to solve this task in an end-to-end fashion using retrieval-based LLMs. Models like Retrieval Augmented Generation (RAG) show surprisingly good performance without involving complex information extraction pipelines. However, the results achieved by these models on the two slot filling tasks in the KILT benchmark are still not at the level required by real-world information extraction systems. In this paper, we describe several strategies we adopted to improve the retriever and the generator of RAG in order to make it a better slot filler. Our KGI0 system (available at https://github.com/IBM/retrieve-write-slot-filling) reached the top-1 position on the KILT leaderboard on both T-REx and zsRE dataset with a large margin.

• The paper introduces KGI0, a zero-shot learning system that improves slot filling performance on the T-REx and zsRE datasets by 24% and 18%, respectively.
• It combines tailored retriever and generator workflows, effectively reducing the manual effort required for extracting knowledge graphs from unstructured text.
• The research highlights the promise of transformer-based models for domain adaptation and scalable, automatic information retrieval.

Zero-shot Slot Filling with DPR and RAG

The paper by Glass, Rossiello, and Gliozzo discusses advancements in zero-shot slot filling through the application of retrieval-based LLMs, specifically Dense Passage Retrieval (DPR) and Retrieval Augmented Generation (RAG). This research addresses the challenge of knowledge graph (KG) extraction from unstructured text corpora with minimal human intervention. The work is embedded within the context of the KILT (Knowledge Intensive Language Tasks) benchmark—a framework intended to standardize the evaluation of AI-driven information retrieval tasks.

Summary of the Research

Slot filling, a task that requires identifying and populating pre-defined relational attributes for given entities, serves as a practical evaluation of automated KG generation capabilities. Traditional solutions have involved intensive pipelines that incorporate named entity recognition, co-reference resolution, and rule-based or supervised relationship extraction. These existing approaches, while capable, demand substantial manual labor for dataset generation, annotation, and rule creation.

The paper introduces $KGI_0$, an innovative system that leverages zero-shot learning with DPR and RAG in an integrated manner, showcasing a substantial improvement over previous methodologies. The system emphasizes fine-tuning the retriever and generator components of RAG for the slot filling task specifically. The $KGI_0$ system displays top-tier performance on the KILT benchmark, notably outstripping existing methods on the T-REx and zsRE datasets by a significant margin in terms of retrieval precision and slot filling accuracy.

Key Findings and Numerical Results

The $KGI_0$ system demonstrated a 24% increase in KILT-F1 score on the T-REx dataset and an 18% increase on the zsRE dataset, setting a precedent for performance in the slot filling domain. These improvements highlight the successful tuning of retrieval components, which can retrieve and rank relevant passages with high precision—a frequent bottleneck in complex data extraction pipelines.

This enhancement in retrieval precision allows $KGI_0$ not only to find more relevant content but also ensures the generation of accurate slot fillers. By using DPR-trained passage encoders and a carefully crafted retrieval plus generation loop, the system effectively marginalizes over potential filler sequences to improve accuracy.

Theoretical and Practical Implications

The achievements of $KGI_0$ accentuate the viability of transformer-based models in domain adaptation and flexible deployment. By streamlining the pipeline into a cohesive retrieval and generation process, the paper suggests a promising direction for rapid, domain-agnostic information extraction systems. The approach diminishes the barrier for KG technology adoption, which has historically been constrained by schema definition and data population efforts.

The success of this research suggests that further exploration into hierarchical retrieval processes, or alternate transformer-based retrieval strategies, may yield even greater efficiencies. The exact balancing of retrieval and generation components remains an open area for further refinement and tuning, as evidenced by GENRE’s high retrieval scores—a potential area for mixed-method optimization.

Regarding future developments, the paper hints at the possibility of extending these models to other data-rich environments beyond Wikipedia, paving the way for scalable, automatic relational database population techniques.

Conclusion

In conclusion, the researched methodologies present a promising advancement in the field of automated KG extraction. By combining tailored retriever-generator workflows and leveraging zero-shot learning configurations, $KGI_0$ achieves state-of-the-art results in slot filling tasks. This research opens avenues for simplified, yet highly effective, information extraction processes—ushering a new stage in the automated construction of knowledge systems.