Summary of Learning Fine-grained Grounded Citations For Attributed Large Language Models, by Lei Huang et al.
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
by Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin
First submitted to arxiv on: 8 Aug 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 paper introduces FRONT, a training framework designed to teach large language models (LLMs) to generate Fine-Grained Grounded Citations, which can improve the verifiability of generated text. The current approaches to attributed LLMs rely on in-context learning and use coarse document identifiers, making it challenging for users to perform fine-grained verification. The proposed framework uses supporting quotes to guide the generation of grounded and consistent responses, improving citation quality and facilitating fine-grained verification. The experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations, with LLaMA-2-7B significantly outperforming all baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching language models to write better notes that can be easily checked. Right now, these models have a problem with making up information and it’s hard for people to fact-check their work. The new method, called FRONT, helps the models by giving them specific quotes from the sources they’re using. This makes their notes more accurate and easier to verify. The results show that this method works well and can even beat some of the best existing methods. |
Keywords
* Artificial intelligence * Llama
