Summary of From Rag to Riches: Retrieval Interlaced with Sequence Generation, by Palak Jain et al.
From RAG to RICHES: Retrieval Interlaced with Sequence Generation
by Palak Jain, Livio Baldini Soares, Tom Kwiatkowski
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The novel approach, RICHES, combines retrieval with sequence generation tasks, eliminating the need for separate retriever and generator in conventional RAG systems. By directly decoding document contents constrained on the corpus, RICHES unifies retrieval with generation, allowing adaptation to new tasks via prompting alone. This approach works with any Instruction-tuned model without additional training, providing attributed evidence, supporting multi-hop retrievals, and planning subsequent retrievals within a single decoding pass of the LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RICHES is a new way to find information on the internet that combines two important tasks: searching for answers and generating text. It can do this all by itself, without needing separate parts for searching and writing. This makes it very good at learning new things and answering complex questions. RICHES uses big language models to figure out what to look for and how to write about it. |
Keywords
» Artificial intelligence » Prompting » Rag
