Add PEFT baselines, ICL baselines, profile-based, and unified pipeline

New baselines:
- baselines/peft_baseline.py: LoRA, Tiny LoRA, VeRA (per-user PEFT adaptation)
- baselines/dense_retrieval.py: Dense retrieval ICL (sentence-transformers)
- baselines/profile_based.py: LLM-generated user profile conditioned generation

New scripts:
- scripts/run_all_methods.py: Unified pipeline running all 9 methods with
  per-method directory output structure (method/per_user.json)
- scripts/run_peft_baselines.py: PEFT-only evaluation (legacy)
- scripts/run_significance.py: Significance tests (UPH+Base per-user)
- scripts/run_uph_base_per_user.py: UPH+Base with full per-user data
- scripts/compute_bertscore.py: BERTScore from saved predictions
- scripts/significance_test.py: Standalone significance test framework

Updated .gitignore to exclude outputs/ directory.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 42 insertions, 0 deletions

diff --git a/baselines/dense_retrieval.py b/baselines/dense_retrieval.py
new file mode 100644
index 0000000..db403a8
--- /dev/null
+++ b/baselines/dense_retrieval.py
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+"""Dense Retrieval ICL baseline.
+
+Uses sentence-transformers for dense retrieval over user support set,
+then places top-K retrieved items as in-context examples.
+"""
+
+import torch
+from sentence_transformers import SentenceTransformer
+
+
+class DenseRetriever:
+    """Dense retriever using sentence-transformers embeddings."""
+
+    def __init__(self, model_name='all-MiniLM-L6-v2', device='cpu'):
+        self.model = SentenceTransformer(model_name, device=device)
+
+    def retrieve_top_k(self, query: str, support_items: list, k: int = 1):
+        """Retrieve top-k support items most relevant to query.
+
+        Args:
+            query: query input text
+            support_items: list of dicts with 'support_input', 'support_output'
+            k: number of items to retrieve
+
+        Returns:
+            List of top-k support items (sorted by relevance)
+        """
+        if len(support_items) <= k:
+            return support_items
+
+        texts = [item['support_input'] for item in support_items]
+        embeddings = self.model.encode([query] + texts, convert_to_tensor=True)
+
+        query_emb = embeddings[0]
+        item_embs = embeddings[1:]
+
+        similarities = torch.nn.functional.cosine_similarity(
+            query_emb.unsqueeze(0), item_embs, dim=1
+        )
+
+        top_indices = similarities.argsort(descending=True)[:k].tolist()
+        return [support_items[i] for i in top_indices]