Update docs/articles_en/openvino-workflow/model-optimization-guide/we…

…ight-compression.rst

Co-authored-by: Alexander Kozlov <[email protected]>

docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst

@@ -237,6 +237,8 @@ trade-offs after optimization:
   There are three modes: INT8_ASYM, INT8_SYM, and NONE, which retains
   the original floating-point precision of the model weights (``INT8_ASYM`` is default value).
 
+**Use synthetic data for LLM weight compression**
+
 It is possible to generate a synthetic dataset using the `nncf.data.generate_text_data` method for
 data-aware weight compression. The method takes a language model (e.g. from `optimum.intel.openvino`)
 and a tokenizer (e.g. from `transformers`) as input and returns the list of strings generated by the model.