Large language models (LLMs) often achieve high performance in native language identification (NLI) benchmarks by leveraging superficial contextual clues such as names, locations, and cultural stereotypes, rather than the underlying linguistic patterns indicative of native language (L1) influence.To improve robustness, previous work has instructed LLMs to disregard such clues.In this work, we demonstrate that such a strategy is unreliable and model predictions can be easily altered by misleading hints.To address this problem, we introduce an agentic NLI pipeline inspired by forensic linguistics, where specialized agents accumulate and categorize diverse linguistic evidence before an independent final overall assessment.In this final assessment, a goal-aware coordinating agent synthesizes all evidence to make the NLI prediction.On two benchmark datasets, our approach significantly enhances NLI robustness against misleading contextual clues and performance consistency compared to standard prompting methods. 1

Simona Emilova Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Elaine Cannon, Gerold Schneider, Benjamin Victor Ineichen. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.