Flatten 1_raw_dataset submodules into plain tracked files

FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were
nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well).
Drop their .git history and track the contents directly in this repo
instead of as submodules/gitlinks.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

Codes/1_raw_dataset/FocalLoRA/README.md

@@ -0,0 +1,54 @@
+## Don’t Forget the Enjoin: FocalLoRA for Instruction Hierarchical Alignment in Large Language Models
+
+### framework
+![framework](figure/framework.png)
+
+### Data Generation
+```
+python dataGeneration.py
+```
+The second type of data is obtained by reversing the system with the user instruction.
+
+### Attention Visualization
+
+visualization_attention.py is used to visualize the attention heatmaps before and after fine-tuning the model. An example script is:
+```
+python visualization_attention.py \
+  --model_path "/home/user/models/Meta-Llama-3.1-8B-Instruct/" \
+  --lora_path "/home/user/LoraAdapter_set/llama3_loraAdapter3_0.3/" \
+  --json_file "json/test.json" \
+  --cuda 0\
+  --important_file "outputs/case_outputs/important_heads.json" \
+  --output_path "./attention_visualization/lora_llama_case" 
+```
+
+To reproduce the conflict-vs-normal case study described in the paper, run the helper script:
+
+```
+./visualize.sh
+```
+
+This script constructs the greenhouse-effect prompts (English-only system, optional French-only user instruction), and renders attention maps for both the base model and the fine-tuned LoRA adapter under `attention_visualization/base_model` and `attention_visualization/finetuned`.
+
+### Model fine-tuning
+```
+python _tuning.py \
+  --model_path "/home/user/models/Meta-Llama-3.1-8B-Instruct/" \
+  --json_path "data/language_instruction.json" \
+  --output_dir "LoraAdapter_set/llama3_loraAdapter3_0.5" \
+  --topk 10 \
+  --epochs 10 \
+  --lr 2e-4 \
+  --lambda_focus 1 \
+  --tune_path tuneData
+```
+
+### Model Output
+```
+python GetAS.py \
+  --json_path data/case_instruction.json\
+  --model_path "/home/user/models/Meta-Llama-3.1-8B-Instruct/" \
+  --lora_path "" \
+  --output_dir "results/llama" \
+  --cuda 1
+```