Leveraging Prompt-Tuning for Bengali Grammatical Error Explanation Using Large Language Models
This addresses the problem of grammatical error explanation for Bengali language learners and educators, but it is incremental as it builds on existing prompt-tuning and LLM techniques.
The paper tackled Bengali Grammatical Error Explanation by proposing a three-step prompt-tuning method using large language models, resulting in GPT-4 achieving a 5.26% improvement in F1 score and a 6.95% improvement in exact match over the baseline, though it still lags behind human performance.
We propose a novel three-step prompt-tuning method for Bengali Grammatical Error Explanation (BGEE) using state-of-the-art large language models (LLMs) such as GPT-4, GPT-3.5 Turbo, and Llama-2-70b. Our approach involves identifying and categorizing grammatical errors in Bengali sentences, generating corrected versions of the sentences, and providing natural language explanations for each identified error. We evaluate the performance of our BGEE system using both automated evaluation metrics and human evaluation conducted by experienced Bengali language experts. Our proposed prompt-tuning approach shows that GPT-4, the best performing LLM, surpasses the baseline model in automated evaluation metrics, with a 5.26% improvement in F1 score and a 6.95% improvement in exact match. Furthermore, compared to the previous baseline, GPT-4 demonstrates a decrease of 25.51% in wrong error type and a decrease of 26.27% in wrong error explanation. However, the results still lag behind the human baseline.