Summary of Reliable, Adaptable, and Attributable Language Models with Retrieval, by Akari Asai et al.
Reliable, Adaptable, and Attributable Language Models with Retrieval
by Akari Asai, Zexuan Zhong, Danqi Chen, Pang Wei Koh, Luke Zettlemoyer, Hannaneh Hajishirzi, Wen-tau Yih
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper advocates for replacing parametric language models (LMs) with retrieval-augmented LMs as the next generation. Parametric LMs excel at flexibility and capability but struggle with hallucinations, adapting to new data distributions, and verifiability. Retrieval-augmented LMs address these issues by incorporating large-scale datastores during inference. Despite potential, they are hindered by limitations in leveraging helpful text beyond knowledge-intensive tasks, restricted interaction between components, and infrastructure scaling challenges. The authors propose a roadmap for developing general-purpose retrieval-augmented LMs, focusing on datastores and retrievers, pipeline improvements, and efficient training/inference infrastructure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to have a conversation with a computer program that can understand and respond to human language. This is the goal of “language models”. While they’re very good at understanding language, they still have some major limitations. One way to make them better is by using a combination of pre-existing text and new information to answer questions or complete tasks. The authors of this paper think that this approach, called “retrieval-augmented LMs”, has the potential to solve many of the current limitations of language models. However, they also recognize some challenges in making this approach work. To overcome these challenges, they propose a plan for developing better retrieval-augmented LMs. |
Keywords
* Artificial intelligence * Inference
