Summary of Eliciting Critical Reasoning in Retrieval-augmented Language Models Via Contrastive Explanations, by Leonardo Ranaldi et al.
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations
by Leonardo Ranaldi, Marco Valentino, Andrè Freitas
First submitted to arxiv on: 30 Oct 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 This paper explores how to improve the capabilities of Large Language Models (LLMs) when retrieving factual context through retrieval-augmented generation (RAG). Current RAG mechanisms struggle with noisy contexts, leading to incorrect inferences and hallucinations. To address this, the authors propose Contrastive-RAG (C-RAG), a framework that retrieves relevant documents, selects passages, generates explanations, and builds contrastive reasoning demonstrations from LLMs to instruct smaller models for retrieval-augmented tasks. Experimental results show that C-RAG improves state-of-the-art RAG models while requiring fewer prompts and being robust to perturbations in retrieved documents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to answer a tricky question by searching through lots of books. You want to make sure you get the right answer, but sometimes it’s hard to find the right information. This paper is about how to improve this process using special computers called Large Language Models (LLMs). The authors found that these LLMs struggle when trying to figure out what’s important and what’s not in a lot of text. They came up with an idea called Contrastive-RAG, which helps the LLMs understand what’s important by giving them examples and explanations. This makes it easier for smaller computers to learn from the bigger ones. The authors tested this idea and found that it works really well! |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
