Despite representing nearly one-third of the world’s languages, African languages remain critically underserved by modern NLP technologies, with 88% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and empirical analysis. Our contributions include: (1) a quality-controlled data collection pipeline, yielding the largest validated African multi-modal speech and text dataset spanning 40 languages with 19 billion text tokens and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that even modest-scale models, when fine-tuned on targeted language data, achieve substantial improvements over untrained baselines, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a comparative analysis against Google Translate in which a 1B-parameter model matched or surpassed the commercial system in several languages including Yoruba and Twi, revealing that data scarcity, rather than model scale, constitutes the primary bottleneck for low-resource NLP, and suggesting that systematic dataset development yields disproportionate returns for low-resource languages.

Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs’ performance in understanding and interpreting financial regulations has rarely been studied. Therefore, we organize the Regulations Challenge, a shared task at COLING FinNLP-FNP-LLMFinLegal-2025. It encourages the academic community to explore the strengths and limitations of popular LLMs. We create 9 novel tasks and corresponding question sets. In this paper, we provide an overview of these tasks and summarize participants’ approaches and results. We aim to raise awareness of FinLLMs’ professional capability in financial regulations and industry standards.

Despite the promise of large language models based agent framework in stock trading task, their capabilities for comprehensive analysis and multiple different financial assets remain largely unexplored, such as cryptocurrency trading. To evaluate the capabilities of LLM-based agent framework in cryptocurrency trading, we introduce an LLMs-based financial shared task featured at COLING 2025 FinNLP-FNP-LLMFinLegal workshop, named Agent-based Single Cryptocurrency Trading Challenge. This challenge includes two cryptocurrencies: BitCoin and Ethereum. In this paper, we provide an overview of these tasks and datasets, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing cryptocurrency trading challenges. To the best of our knowledge, the Agent-based Single Cryptocurrency Trading Challenge is one of the first challenges for assessing LLMs in the financial area. In consequence, we provide detailed observations and take away conclusions for future development in this area.

Despite the promise of large language models (LLMs) in finance, their capabilities for financial misinformation detection (FMD) remain largely unexplored. To evaluate the capabilities of LLMs in FMD task, we introduce the financial misinformation detection shared task featured at COLING FinNLP-FNP-LLMFinLegal-2024, FMD Challenge. This challenge aims to evaluate the ability of LLMs to verify financial misinformation while generating plausible explanations. In this paper, we provide an overview of this task and dataset, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing the FMD task. To the best of our knowledge, the FMD Challenge is one of the first challenges for assessing LLMs in the field of FMD. Therefore, we provide detailed observations and draw conclusions for the future development of this field.