Advocating for Retrieval-Augmented Language Models Over Parametric LMs

Основні поняття

Retrieval-augmented language models offer reliability, adaptability, and attributability over parametric models. The paper advocates for their widespread adoption through advancements in architecture, training methodologies, and infrastructure.

Анотація

Retrieval-augmented language models (LMs) are proposed as the next generation of LMs to address limitations faced by parametric LMs. The paper discusses the challenges faced by parametric LMs and the potential benefits of retrieval-augmented LMs. It emphasizes the need for advancements in architecture, training methodologies, and infrastructure to promote the adoption of retrieval-augmented LMs across diverse domains.
The content delves into the weaknesses of parametric LMs such as factual inaccuracies, difficulty in verification, challenges in adapting to new data distributions, prohibitively large model sizes, and more. It highlights how retrieval-augmented LMs can mitigate these issues by leveraging external datastores during inference.
The paper outlines a roadmap for advancing retrieval-augmented LMs by rethinking retrieval and datastores, enhancing interactions between retrievers and LMs, and building better systems for scaling and adaptation. It emphasizes collaborative efforts across interdisciplinary areas to achieve these advancements.

Статистика

Large-scale text data is used during training for parametric language models.
Retrieval-augmented LM leverages an external datastore at inference.
Some approaches involve retrieving text chunks or tokens from the datastore.
Datastores can contain billions of tokens for effective performance.
Retrieval errors are prominent issues in retrieval-augmented LM systems.

Цитати

"Retrieval-augmented language models offer reliability, adaptability, and attributability over parametric models."
"Efforts are needed to develop robust intelligent systems based on retrieval-augmented LMs that surpass fully parametric LMs."
"The community needs to focus on scalable architectures and efficient end-to-end training methods for retrieval-augmented LM systems."

Ключові висновки, отримані з

Reliable, Adaptable, and Attributable Language Models with Retrieval

by Akari Asai,Z... о arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.03187.pdf

Глибші Запити

How can we ensure that retrieved context is relevant beyond semantic or lexical similarity?

To ensure that retrieved context goes beyond semantic or lexical similarity, we need to redefine what constitutes relevance in the context of retrieval-augmented language models. One approach could be to incorporate more diverse notions of relevance based on the specific task requirements. This may involve considering factors such as underlying reasoning patterns, writing style, or contextual information that may not exhibit direct semantic or lexical similarities with the input query.
Additionally, developing a versatile retriever capable of adjusting its search behavior based on different notions of similarity and additional input could enhance the relevancy of retrieved context. By exploring methods for contextualized retrieval rather than relying solely on traditional metrics like semantic or lexical overlap, we can improve the effectiveness and applicability of retrieval-augmented LMs across a broader range of tasks.

What are the implications of limited interactions between retrievers and LMs on overall system performance?

The limited interactions between retrievers and LMs can have significant implications for overall system performance in retrieval-augmented language models. When there are shallow interactions between these components, it can lead to issues such as unsupported generations, susceptibility to irrelevant text, and challenges in handling information from multiple documents effectively.
Without deep integration and collaboration between retrievers and LMs throughout both training and inference stages, the system may struggle to leverage retrieved context efficiently. This lack of interaction hampers the model's ability to make informed decisions based on relevant information extracted from the datastore. As a result, this limitation can impact the model's accuracy, reliability, adaptability, and overall performance across various tasks.

How can standardized open-source implementations enhance the adoption of retrieval-augmented LM pipelines?

Standardized open-source implementations play a crucial role in enhancing the adoption of retrieval-augmented LM pipelines by providing practitioners with accessible tools and resources for building robust systems. These standardized implementations offer consistent frameworks for developing retrieval-augmented LMs across different architectures and training methodologies.
By having open-source libraries specifically tailored for retrieval-based models, researchers and developers can easily experiment with various approaches without starting from scratch each time. This accelerates innovation by reducing development time while promoting reproducibility within the research community.
Moreover, standardized implementations facilitate collaboration among researchers working on similar problems by establishing common benchmarks and evaluation metrics. This fosters knowledge sharing and advancements in best practices for building effective retrieval-augmented LM systems.
In summary,

Standardized open-source implementations streamline development processes.
They encourage collaboration among researchers.
They promote reproducibility within the research community.
They establish common benchmarks for evaluating model performance.
They accelerate innovation by providing accessible tools for experimentation.