KILT: a Benchmark for Knowledge Intensive Language Tasks (2009.02252v4)

Abstract: Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearch/KILT.

• The paper introduces a unified benchmark that evaluates models across 11 knowledge-intensive tasks anchored to a consistent Wikipedia snapshot.
• It demonstrates that retrieval-augmented generation using seq2seq with dense passage retrieval yields superior evidence-based responses.
• The paper establishes a framework for task-agnostic model evaluation with provenance tracking, fostering advances in both theory and application.

KILT: A Benchmark for Knowledge Intensive Language Tasks

The paper "KILT: a Benchmark for Knowledge Intensive Language Tasks" presents a comprehensive framework aimed at benchmarking models that engage with knowledge-intensive NLP tasks using a unified knowledge base, specifically a consistent snapshot of Wikipedia. The authors address the inherent complexity in designing and evaluating models that require access to extensive information sources, proposing KILT as a standardized benchmark suite that integrates multiple tasks with a shared foundation for better comparability and advancement in model performance.

Overview

KILT aggregates eleven datasets across five distinct NLP tasks: fact checking, entity linking, slot filling, open-domain question answering, and dialogue. By anchoring these tasks to the same Wikipedia snapshot, KILT significantly reduces the complexity involved in engineering diverse task-specific solutions and promotes the development of generalized, task-agnostic memory architectures. Importantly, the benchmark requires models to not only perform well on task-specific metrics but also demonstrate the ability to retrieve and provide supporting evidence for their outputs.

Numerical Results and Models

The paper details empirical evaluations using both task-specific and general models, highlighting the strength of explicitly retrieved information. Between the models, a seq2seq architecture combined with dense passage retrieval (DPR) illustrates superior performance in producing high-quality responses, establishing notable results over multiple tasks within KILT. The authors emphasize that RAG, which allows explicit knowledge retrieval within the generation process, yields competitive performance, showcasing the potential for integrating retrieval-augmented generation techniques in knowledge-based tasks.

Contributions and Claims

Key contributions of KILT include:

1. Unified Benchmark: Establishes a standard for evaluating models across knowledge-intensive tasks using a common Wikipedia snapshot.
2. General Model Evaluation: Enables testing of task-agnostic models, emphasizing the necessity of evidence-retrieval mechanisms.
3. Comprehensive Dataset: Compiles tasks requiring a range of interactions with the knowledge base, from structured queries to open-ended dialogue.
4. Provenance Tracking: Incorporates provenance for output justification, enhancing interpretability and trust in model predictions.

Despite challenges such as aligning disparate datasets to a common knowledge source and maintaining relevance against evolving task requirements, KILT provides a robust foundation for advancing research in NLP models requiring extensive knowledge integration.

Implications and Future Directions

The implications of KILT are significant in both theoretical exploration and practical applications:

• Theoretical Impact: Encourages the exploration of integrated architectures that utilize both parametric memory and non-parametric retrieval, potentially influencing future model designs.
• Practical Applications: Offers a pathway for developing real-world systems capable of informed, contextually aware engagements across varied knowledge domains.

Future research might focus on enhancing retrieval mechanisms and refining memory architectures, potentially leveraging KILT's integrated approach to bolster task-general models that can dynamically access and apply knowledge. Furthermore, advancements in evaluating model provenance alongside performance could lead to greater interpretability and model transparency, crucial for sensitive applications such as fact-checking and public information dissemination.

In conclusion, KILT stands as a formidable addition to natural language processing benchmarks, inviting improvements in how models engage with and reason about large knowledge corpora.