Summary of Can Long-context Language Models Subsume Retrieval, Rag, Sql, and More?, by Jinhyuk Lee et al.
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
by Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Long-context language models (LCLMs) are transforming the way we approach tasks that previously relied on external tools. By natively processing entire corpora of information, LCLMs eliminate the need for specialized knowledge of tools, provide robust end-to-end modeling, and enable sophisticated prompting techniques. To evaluate this paradigm shift, the LOFT benchmark is introduced, featuring real-world tasks requiring context up to millions of tokens, designed to assess LCLM performance on in-context retrieval and reasoning. Findings reveal that LCLMs rival state-of-the-art retrieval and RAG systems without explicit training. However, challenges persist in areas like compositional reasoning required for SQL-like tasks. Prompting strategies significantly influence performance, highlighting the need for continued research as context lengths grow. The LOFT benchmark provides a rigorous testing ground for LCLMs, showcasing their potential to supplant existing paradigms and tackle novel tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way of doing things in computers called “long-context language models.” These models can understand really long pieces of text and do things with it. It makes some tasks easier because you don’t need to know how to use special tools anymore. The researchers tested these models on some real-world problems and found that they are actually pretty good at solving them, even better than some other methods. However, there’s still some work to be done to make these models perfect. The goal is to make computers smarter by using these long-context language models. |
Keywords
» Artificial intelligence » Prompting » Rag
