Recent advances in Large Language Models (LLMs) have significantly improved Text-to-SQL performance in high-resource languages. However, their effectiveness in low-resource language settings remains largely underexplored. In this work, we investigate the adaptation of LLMs for Text-to-SQL generation in Chichewa, a low-resource Bantu language spoken by over 12 million people in Malawi and neighboring regions. We construct a structured Chichewa Text-to-SQL benchmark consisting of 400 manually curated natural language–SQL pairs grounded in a unified relational database covering agriculture, commodity prices, population statistics, market data, and food insecurity. We systematically evaluate five open-source LLMs under zero-shot, random 5-shot, and retrieval-augmented 5-shot prompting, in both English and Chichewa. We then apply parameter-efficient fine-tuning (QLoRA) to selected models and, crucially, evaluate the combined effect of QLoRA fine-tuning with retrieval-augmented prompting. QLoRA alone improves English execution accuracy to 78.3% and Chichewa execution accuracy to 41.7%. When combined with retrieval-augmented prompting, QLoRA achieves 53.3% execution accuracy in Chichewa, representing the best reported result for this language on this benchmark and narrowing the English–Chichewa gap to 23.4 percentage points. Our findings offer practical guidance for deploying database interfaces in linguistically underserved environments.
John Emeka Eze, Dunstan Matekenya, Evance Mathewe· Annual International ACM SIG...· 0 citations
This work formalizes the hybrid LLM-planner and RL-controller architecture as a Goal-Augmented Markov Decision Process and shows that when the LLM per-state progress score is used as a bounded potential function, the resulting shaping term preserves the optimal policy set even when the LLM scores are inaccurate.
Christophe D. Hounwanou, John Emeka Eze, Yaé Ulrich Gaba· 0 citations
Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimization. The proposed architecture leverages the LLM to generate subgoals, structured plans, and contextual guidance, while the RL agent refines low-level actions through interaction with the environment. Experiments on sequential decision tasks demonstrate improved sample efficiency, higher success rates, and more coherent action trajectories compared to RL-only and LLM-only baselines. This hybrid paradigm highlights a promising direction for building more capable autonomous systems.
Christophe D. Hounwanou, John Emeka Eze, Yaé Ulrich Gaba· 0 citations