This modelcard documents FM-FCI/DateArith-VLSP2025, a Vietnamese LLM fine-tuned for date arithmetic task. It achieved #1 in the VLSP 2025 benchmark for date-arith task.

This work investigates two subtasks in temporal reasoning: 1. Date Arithmetic (datearith) and 2. Duration Question Answering (durationQA). For date-arith, we focus on finetuning large language models (LLMs) to directly extract and compute answers. For durationQA, the challenge lies in identifying both explicit and implicit duration expressions in text and reasoning with world knowledge to assess correctness. We explore multiple approaches, from naive supervised fine-tuning (SFT) to SFT augmented with reasoning-based synthetic data and GRPO. Our findings highlight the critical role of carefully constructed data and appropriate training strategies in enabling effective temporal reasoning.

40,000 synthetic samples

Đánh giá dựa vào tập valid, public test, private test mà BTC cung cấp

Accuracy

Độ chính xác trên public test là 98% và trên private test là 99%

Enabling Temporal Commonsense in Vietnamese LLMs – Date-Arith and DurationQA

Duc Dinh Chu*, Thanh-Bac Nguyen Ba*, Duy Dinh Le, Khanh Van Tran