The claim matching (CM) task can benefit an automated fact-checking pipeline by putting together claims that can be resolved with the same fact-check. In this work, we are the first to explore zero-shot and few-shot learning approaches to the task. We consider CM as a binary classification task and experiment with a set of instruction-following large language models (GPT-3.5-turbo, Gemini-1.5-flash, Mistral-7B-Instruct, and Llama-3-8B-Instruct), investigating prompt templates. We introduce a new CM dataset, ClaimMatch, which will be released upon acceptance. We put LLMs to the test in the CM task and find that it can be tackled by leveraging more mature yet similar tasks such as natural language inference or paraphrase detection. We also propose a pipeline for CM, which we evaluate on texts of different lengths.

A floating solar farm off the coast of Thailand, a $34 million project, will be monitored by Percepto drones. Percepto announced that it has completed a proof-of-concept (POC) with the Electric Generating Authority of Thailand (EGAT) to monitor a 250-acre floating solar farm. The farm is the size of 70 soccer fields and is located 350 m from the nearest shoreline. Percepto's AIM software and drone-in-a-box solution will autonomously perform routine inspections of panels and other equipment to detect anomalies and ensure everything is operating properly. The drones will provide regular operations and maintenance reports, map the location of the panels; and perform inspections of substations, transformers, floating fences and solar floaters, which hold the solar panels above water.