Reliable, Adaptable, and Attributable Language Models with Retrieval

Advancing LLMs through Retrieval-Augmentation

Introduction to Retrieval-Augmented Language Models

In the ever-evolving landscape of NLP, the advent of LLMs such as GPT-4 represents a significant milestone due to their unprecedented understanding and generation capabilities. Despite these advancements, traditional parametric LMs exhibit notable weaknesses, including proneness to factual inaccuracies, difficulty in adaptation to new data distributions, and challenges in verifying the source of generated content. This position paper articulates a compelling argument for the transition towards retrieval-augmented language models (RALMs) as a superior alternative, capable of addressing these critical limitations by leveraging external datastores for inference.

The Limitations of Parametric LMs

Parametric LMs, by design, ingest extensive web data during training to internalize knowledge into their parameters. Despite their successes, these models are inherently limited by several factors:

• Factual accuracies: The tendency to produce hallucinations or factual inaccuracies is notably heightened in parametric LMs, especially when dealing with long-tail knowledge.
• Verification challenges: The output of these models often lacks clear attributions, making verification of generated content a daunting task.
• Adaptation costs: The static nature of trained parameters complicates adaptation to new data distributions, requiring extensive retraining or fine-tuning.
• Model scalability: The increasing size of LMs poses computational and environmental challenges, with diminishing returns on scaling up the model size.

The Promise of Retrieval-Augmented LMs

Retrieval-augmented LMs distinguish themselves by dynamically incorporating relevant external information from a large-scale datastore during inference, effectively addressing the limitations of parametric models:

• They demonstrate a notable reduction in factual errors by directly referencing up-to-date external sources.
• The model outputs are more easily verifiable and attributable due to the explicit use of external documents.
• Flexibility in adapting to new information and data distributions is inherently improved through the update of the external datastore.
• These models achieve higher parameter efficiency by not needing to internalize all knowledge within the model parameters.

Empirical Evidence Supporting RALMs

Retrieval-augmented LMs have shown considerable promise in various tasks, outperforming their parametric counterparts in many respects:

• Knowledge-intensive tasks such as question answering and fact verification benefit significantly from direct access to source documents.
• Domain adaptation is more effectively achieved by tailoring the datastore to include domain-specific information.
• Model efficiency is demonstrated through the ability of RALMs to compete with or exceed the performance of larger parametric models with a fraction of the parameters.

Challenges and Future Directions

Despite their potential, the widespread adoption of RALMs is hampered by several challenges:

• Datastore and retriever functionality: The current reliance on semantic or lexical similarity limits the model's effectiveness beyond conventional tasks.
• Retriever-LM interaction: The limited interaction between retrieval and generation components in current models restricts their full potential.
• Infrastructure for scaling: There lacks a cohesive effort to develop infrastructure capable of supporting the efficient training and inference of RALMs at scale.

To overcome these obstacles, the paper proposes a comprehensive roadmap focusing on innovative architecture development, advanced training methodologies, and the creation of specialized infrastructure. The suggested improvements include redefining the notion of "relevance" for retrieval, exploring new model architectures that facilitate deeper integration between retrieval and language model components, and investing in open-source efforts and standardized implementations to lower the entry barrier for research on retrieval-augmented models.

Conclusion and Impact

The transition towards retrieval-augmented language models heralds a new era in NLP, promising improvements in reliability, adaptability, and attributability of generated outputs. This paper not only highlights the limitations of current parametric LMs but also charts a course for future research aimed at realizing the full potential of RALMs across a broader spectrum of applications. However, it is imperative to continue evaluating these models critically, ensuring they meet the evolving demands of real-world applications without introducing new forms of biases or inaccuracies.