  The rapid evolution of large language models (LLMs) represents a substantial
leap forward in natural language understanding and generation. However,
alongside these advancements come significant challenges related to the
accountability and transparency of LLM responses. Reliable source attribution
is essential to adhering to stringent legal and regulatory standards, including
those set forth by the General Data Protection Regulation. Despite the
well-established methods in source attribution within the computer vision
domain, the application of robust attribution frameworks to natural language
processing remains underexplored. To bridge this gap, we propose a novel and
versatile TRansformer-based Attribution framework using Contrastive Embeddings
called TRACE that, in particular, exploits contrastive learning for source
attribution. We perform an extensive empirical evaluation to demonstrate the
performance and efficiency of TRACE in various settings and show that TRACE
significantly improves the ability to attribute sources accurately, making it a
valuable tool for enhancing the reliability and trustworthiness of LLMs.
