From Understanding Korean Culture to Screen Control: Everything About Kanana-V Feature Expansion

Kanana-V expands a vision-language model beyond single-image question answering into Korean cultural understanding, document analysis, multi-image reasoning, and GUI interaction. The post details how Kakao built and evaluated these capabilities through large-scale data curation, Korean benchmarks, and training optimizations. Its central conclusion is that language- and task-specific data quality, rather than scale alone, is essential for producing a practical multimodal model.

Expanding VLM Capabilities

• Real-world VLM applications require more than interpreting one image:
  • Understanding long PDF documents
  • Comparing multiple images
  • Interpreting and operating graphical user interfaces
• Kanana-V targets these requirements through:
  • Korean-context understanding
  • Document and PDF comprehension
  • Multi-image and long-context processing
  • GUI grounding for Computer Use Agents (CUAs)
• Compared with the similarly sized Qwen3-VL 4B, it achieved broadly comparable results and showed particular strength on Korean-language tasks.

Curating Korean Interleaved Data

• Interleaved datasets alternate images and text, as in blogs, enabling broad knowledge acquisition and stronger in-context learning.
• The source collection reached hundreds of terabytes and contained substantial low-quality material, including advertisements, broken images, and duplicated posts.
• Kakao used Hugging Face’s Datatrove framework to shard the data and run filtering pipelines in parallel.

Eight-Stage Data-Cleaning Pipeline

• Image-based document filtering

  • Removed broken, tiny, low-resolution, or extreme-aspect-ratio images.
  • Excluded documents left without valid images.
  • Used thresholds such as an aspect ratio above 3.0 or dimensions below 28 pixels.

• Language identification

  • Applied FastText-based detection.
  • Retained documents with at least 90% probability of being Korean.
  • Preserved Korean technical content containing English quotations or code.

• Gopher repetition filtering

  • Detected repeated lines, paragraphs, and abnormal 2-gram through 10-gram patterns.
  • Removed spam and automatically generated advertising content.

• Gopher quality filtering

  • Adapted English-oriented rules for Korean.
  • Lowered the minimum average word length to one character because Korean tokenization often produces short tokens.
  • Added Korean particles and endings to stopword checks.

• C4 sentence-structure filtering

  • Required at least four sentences.
  • Avoided punctuation-based filtering because Korean writing often omits sentence-final periods.

• FineWeb quality filtering

  • Examined short-line ratios, bullet-list frequency, and lines ending in ellipses.
  • Removed product lists, menus, and similarly unsuitable formats.

• MinHash deduplication

  • Used MinHash and locality-sensitive hashing to efficiently identify copied or highly similar documents without performing all pairwise comparisons.

• PII processing

  • Masked Korean phone numbers, email addresses, and other personal information.
  • Cleaned empty text nodes created by image removal and merged adjacent text blocks.

Impact of Filtering

• Approximately 77% of the original data was removed, leaving 23% for training.
• Ablation experiments showed that filtered data generally improved performance:
  • MMVet increased from 33.76 to 36.79.
  • LLaVA-Wild increased from 75.10 to 78.00.
  • Korean entity recognition increased from 50.05 to 53.66.
  • Korean food-menu understanding increased from 44.56 to 47.02.
• Korean chart understanding was the exception, declining slightly from 58.33 to 57.43.
• The team emphasizes:
  • Running inexpensive filters before costly ones
  • Saving intermediate outputs for inspection and reuse
  • Tuning thresholds for each language
  • Cleaning related text whenever images are removed from interleaved data

The article’s practical recommendation is to treat multimodal model development as an end-to-end data and systems problem: carefully curate culturally relevant data, build language-specific evaluation sets, and optimize training pipelines for each target capability rather than relying solely on larger datasets or models.