InternVL (full fine-tuning) and Vintern-1B-v2 (LoRA) were evaluated on the same Vietnamese chart dataset for direct comparison.
Metric Comparison
InternVL-FT vs Vintern-LoRA
Metric
InternVL-FT
Vintern-LoRA
Improvement (Vintern vs InternVL)
BLEU
0.253
0.468
+85.0%
METEOR
0.512
0.703
+37.3%
ROUGE-1
0.621
0.778
+25.3%
ROUGE-2
0.476
0.676
+42.0%
ROUGE-L
0.567
0.735
+29.6%
BERTScore
0.837
0.903
+7.9%
Vintern-LoRA outperforms InternVL-FT across every metric, even though InternVL was fully fine-tuned while Vintern only used LoRA. The largest gaps appear on BLEU (+85.0%) and ROUGE-2 (+42.0%), indicating Vintern-LoRA generates answers with notably better n-gram and phrase-level overlap with the ground truth. On BERTScore, both models score relatively high, but Vintern-LoRA still holds a consistent edge (0.837 → 0.903).
Conclusion
Despite using a lighter-weight LoRA fine-tuning approach, Vintern-1B-v2 achieves stronger results than a fully fine-tuned InternVL model on the Vietnamese chart QA task, highlighting both the efficiency of LoRA and the suitability of Vintern as the base model for this task.
