Large language models (LLMs) have demonstrated an impressive ability to synthesize plausible and fluent text. However they remain vulnerable to hallucinations, and thus their outputs generally require manual human verification for high-stakes applications, which can be timeconsuming and difficult. This paper proposes symbolically grounded generation (SymGen) as a simple approach for enabling easier validation of an LLM's output. SymGen prompts an LLM to interleave its regular output text with explicit symbolic references to fields present in some conditioning data (e.g., a table in JSON format). The references can be used to display the provenance of different spans of text in the generation, reducing the effort required for manual verification. Across data-to-text and question answering experiments, we find that Figure 1: Compare a standard LLM-generated (A) with LLMs are able to directly output text that makes a SymGen (B, ours) description of a basketball game, use of symbolic references while maintaining based on statistics about it.

RegTech Napier has announced its AI-led technology has been chosen by emerging Norwegian FinTech, ZTL Payment Solution. Napier will supply the B2B payment provider with Transaction Screening, Transaction Monitoring and Client Screening. These tools will enhance ZTL's ability to identify suspicious activity related to money laundering through transaction monitoring, while also identifying potential risk of breaching sanctions with screening solutions. Andreas Bjerke, CEO at ZTL, said: "We are one of the first Norwegian licensed payment providers and we are already seeing rapid growth for our unique service. One of our main priorities is AML compliance and we needed a more flexible AML compliance tool that will be agile and robust enough to grow with us as we scale to enter more global markets and add more products. We aim to stay at the very cutting-edge of online B2B payments and Napier's modern solution will enable us to deliver excellent service while meeting regulatory requirements."

The pandemic has forced criminal gangs to come up with new ways to move money around. In turn, this has upped the stakes for anti-money laundering (AML) teams tasked with detecting suspicious financial transactions and following them back to their source. Key to their strategies are new AI tools. While some larger, older financial institutions have been slower to adapt their rule-based legacy systems, smaller, newer firms are using machine learning to look out for anomalous activity, whatever it might be. It is hard to assess the exact scale of the problem.