Summary of Trace: Transformer-based Attribution Using Contrastive Embeddings in Llms, by Cheng Wang et al.
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs
by Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Transformer-based Attribution framework using Contrastive Embeddings (TRACE) is proposed to address the challenges of reliable source attribution in large language models (LLMs). The rapid evolution of LLMs has led to significant advancements in natural language understanding and generation, but also raises concerns about accountability and transparency. TRACE exploits contrastive learning for source attribution, demonstrating improved accuracy and efficiency in various settings. This framework is particularly valuable for enhancing the reliability and trustworthiness of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to tell where information comes from in big language models has been invented. Right now, these models are getting really good at understanding and generating text, but this makes it hard to know what’s true or not. To fix this, scientists created a special tool called TRACE that helps figure out where the information is coming from. This tool is super helpful for making sure big language models can be trusted. |
Keywords
* Artificial intelligence * Language understanding * Transformer
