Summary of Sfr-rag: Towards Contextually Faithful Llms, by Xuan-phi Nguyen et al.
SFR-RAG: Towards Contextually Faithful LLMs
by Xuan-Phi Nguyen, Shrey Pandit, Senthil Purushwalkam, Austin Xu, Hailin Chen, Yifei Ming, Zixuan Ke, Silvio Savarese, Caiming Xong, Shafiq Joty
First submitted to arxiv on: 16 Sep 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 Retrieval Augmented Generation (RAG) is a paradigm that combines large language models (LLMs) with external contextual information to improve factual accuracy and relevance. To achieve this, LLMs must accurately comprehend provided context, avoid hallucination, handle unanswerable questions, perform complex reasoning, and generate reliable citations. This paper introduces SFR-RAG, a small LLM instruction-tuned for context-grounded generation and hallucination minimization. The authors also propose ContextualBench, a new evaluation framework compiling popular RAG benchmarks like HotpotQA and TriviaQA with consistent settings to ensure reproducibility. Experimental results show that the SFR-RAG-9B model outperforms leading baselines GPT-4o and Command-R+ (104B) in 3 out of 7 ContextualBench benchmarks, achieving state-of-the-art results with fewer parameters. The model remains resilient to contextual alterations and performs well in general instruction-following tasks and function-calling capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve the accuracy of artificial intelligence models that generate text based on information they are given. Right now, these models can sometimes make mistakes or create false information. To fix this, researchers have developed a new approach called Retrieval Augmented Generation (RAG). This approach uses small language models that are trained to work well with contextual information and avoid making mistakes. The authors of this paper tested their approach on several benchmark tests and found that it performed better than other approaches in many cases. They also created a new evaluation framework to help compare different approaches in the future. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Rag » Retrieval augmented generation
