InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval (2307.04601v1)

Abstract: Recent work has explored LLMs to overcome the lack of training data for Information Retrieval (IR) tasks. The generalization abilities of these models have enabled the creation of synthetic in-domain data by providing instructions and a few examples on a prompt. InPars and Promptagator have pioneered this approach and both methods have demonstrated the potential of using LLMs as synthetic data generators for IR tasks. This makes them an attractive solution for IR tasks that suffer from a lack of annotated data. However, the reproducibility of these methods was limited, because InPars' training scripts are based on TPUs -- which are not widely accessible -- and because the code for Promptagator was not released and its proprietary LLM is not publicly accessible. To fully realize the potential of these methods and make their impact more widespread in the research community, the resources need to be accessible and easy to reproduce by researchers and practitioners. Our main contribution is a unified toolkit for end-to-end reproducible synthetic data generation research, which includes generation, filtering, training and evaluation. Additionally, we provide an interface to IR libraries widely used by the community and support for GPU. Our toolkit not only reproduces the InPars method and partially reproduces Promptagator, but also provides a plug-and-play functionality allowing the use of different LLMs, exploring filtering methods and finetuning various reranker models on the generated data. We also made available all the synthetic data generated in this work for the 18 different datasets in the BEIR benchmark which took more than 2,000 GPU hours to be generated as well as the reranker models finetuned on the synthetic data. Code and data are available at https://github.com/zetaalphavector/InPars

• The paper introduces a unified toolkit that generates reproducible synthetic data for neural IR using novel LLM prompting and filtering strategies.
• It implements dual approaches, InPars and Promptagator, enabling robust query generation and selection to enhance training and evaluation.
• Experiments show that models trained on the synthetic data can outperform benchmarks like BM25, demonstrating significant performance gains.

Overview of "InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval"

The paper "InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval" presents a comprehensive toolkit aimed at facilitating the reproducible generation of synthetic data for Neural Information Retrieval (IR) using LLMs. Recent developments have highlighted the potential of LLMs in creating synthetic datasets that can compensate for the paucity of labeled training data in IR tasks, elevating the performance of existing models in scenarios where obtaining human-annotated data is challenging.

Core Contributions

The paper's primary contribution is the provision of a toolkit that unifies the entire synthetic data pipeline—from generation to evaluation. The toolkit allows researchers to reproduce and extend methods for synthetic query generation, filtering, and model training while ensuring compatibility with GPU infrastructures, hence democratizing access beyond the privileged TPU-based implementations. The toolkit integrates seamlessly with major IR libraries and supports a wide range of LLMs, making it flexible for various research agendas.

Methodology

The toolkit implements both the InPars and Promptagator pipelines, each leveraging LLM capabilities to generate high-quality IR datasets:

1. InPars Approach: This method employs static prompts derived from existing datasets, such as MS MARCO, and showcases two prompt variants: the "Vanilla" prompt and "Guided by Bad Questions" (GBQ). The synthetic queries generated are then filtered based on token probability assessments or reranker scores to select high-caliber training data.
2. Promptagator Approach: This method diverges by using dynamic prompts crafted for specific datasets, highlighting its efficacy with domain-specific querying scenarios like the ArguAna dataset. Here, synthetic queries are subject to rigorous filtering using consistency-based strategies facilitated by retriever models, ensuring high relevance between generated queries and documents.

Additionally, the toolkit supports innovative prompting techniques, multiple filtering mechanisms, and trains models like monoT5 3B for reranking tasks. It also harnesses state-of-the-art LLMs such as GPT-J, ensuring the generation of coherent and contextually appropriate synthetic queries.

Results and Discussion

The paper carefully benchmarks models trained using synthetic datasets against existing baselines, such as BM25. Remarkably, models trained on synthetic data frequently outperformed those finetuned on richly labeled datasets. The experiments underscore the effectiveness of synthetic data in enhancing IR performance across various challenging datasets included in the BEIR benchmark. The toolkit’s ability to facilitate robust IR model training on GPU architectures, akin to TPU results, marks a substantial advancement, widening the accessibility of advanced IR research.

Implications and Future Directions

The implications of this research span both theoretical improvements and practical applications in IR systems. The availability of a reproducible toolkit that can widely support research into synthetic data generation democratizes experimental validation and facilitates the development of more generalized retrieval models. The potential to explore different LLMs, prompting strategies, and filtering methods envisions a future where IR systems can be rapidly adapted to novel domains with minimal human intervention. Future work may focus on integrating instruction-tuned LLMs and enhancing consistency filtering, enabling these synthetic datasets to form a backbone for next-gen IR models.

In sum, this work contributes significantly to IR research by providing a flexible, reproducible, and scalable infrastructure that meets the needs of a growing research community intent on harnessing LLMs to overcome data scarcity in neural IR tasks.