With Large Language Model (LLM)-based applications becoming more common due to strong performance across many tasks, prompt optimization has emerged as a way to extract better solutions from frozen, often commercial LLMs that are not specifically adapted to a task. LLM-assisted prompt optimization methods provide a promising alternative to manual/human prompt engineering, where LLM “reasoning” can be used to make them optimizing agents. However, the cost of using LLMs for prompt optimization via commercial APIs remains high, especially for heuristic methods like evolutionary algorithms (EAs), which need many iterations to converge, and thus, tokens, API calls, and rate-limited network overhead. We propose GenDLN, an open-source, efficient genetic algorithm-based prompt pair optimization framework that leverages commercial API free tiers. Our approach allows teams with limited resources (NGOs, non-profits, academics, ...) to efficiently use commercial LLMs for EA-based prompt optimization. We conduct experiments on CLAUDETTE for legal terms of service classification and MRPC for paraphrase detection, performing in line with selected prompt optimization baselines, at no cost.

Hallucinations are one of the major problems of LLMs, hindering their trustworthiness and deployment to wider use cases. However, most of the research on hallucinations focuses on English data, neglecting the multilingual nature of LLMs. This paper describes our submission to the "{textit{SemEval-2025 Task-3 — Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes}}”. We propose a two-part pipeline that combines retrieval-based fact verification against Wikipedia with a BERT-based system fine-tuned to identify common hallucination patterns. Our system achieves competitive results across all languages, reaching top-10 results in eight languages, including English. Moreover, it supports multiple languages beyond the fourteen covered by the shared task. This multilingual hallucination identifier can help to improve LLM outputs and their usefulness in the future.