new jr user study

src/app.py

@@ -47,7 +47,7 @@ def _init_submodule() -> None:
 
         # GitHub serves a tarball of any branch/tag/SHA at this URL.
         # Pinned to a specific commit SHA so future lsp changes don't break us.
-        branch      = "74582acd911f81309ba8b22cef9286c2887dda18"
+        branch      = "862a08160b07aed48238c7dadc191e138a00dc9a"
         tarball_url = f"https://api.github.com/repos/batu-el/lsp/tarball/{branch}"
         tmp_tar     = Path("/tmp/lsp.tar.gz")
         tmp_extract = Path("/tmp/lsp_extract")

src/data.py

@@ -727,7 +727,6 @@ def init_state(cfg: dict) -> dict:
                 "use_demographics":      m.get("use_demographics", False),
                 "use_background":        m.get("use_background", False),
                 "personalization":       m.get("personalization"),
-                "detailed_instruction": m.get("detailed_instruction", True),
             })
 
         for i, slot in enumerate(slots):

src/lsp_wrappers.py

@@ -142,6 +142,7 @@ def build_seller_system_prompt_preference(
     from prompts.seller_system.preference import get_seller_system_prompt
     pv   = cfg["prompt_variant"]
     a, b = pair["product_a"], pair["product_b"]
+    # lsp API: only `personalization` is forwarded (no detailed_instruction / include_bio here).
     result = get_seller_system_prompt(
         personalization=pv["personalization"],
         title=a.get("title"),

src/ui/screens_model_comparison.py

@@ -46,10 +46,11 @@ ETHICS_FINAL = (
 def _prompt_variant_for_model(mconf: dict) -> dict:
     use_d = mconf.get("use_demographics", False)
     use_b = mconf.get("use_background", False)
+    # Only keys read by build_seller_system_prompt_preference → get_seller_system_prompt:
+    # `personalization`. (include_bio is unused there too; bio gated by use_background + format_demographics.)
     return {
         "personalization": mconf.get("personalization", bool(use_d or use_b)),
         "include_bio": bool(mconf.get("use_background", use_b)),
-        "detailed_instruction": mconf.get("detailed_instruction", True),
     }
 
 

src/upload.py

@@ -226,7 +226,8 @@ def _save_and_upload_csv(
                     "personalization",
                     mconf.get("use_demographics", False) or mconf.get("use_background", False),
                 ),
-                "detailed_instruction": mconf.get("detailed_instruction", True),
+                # Not passed to lsp get_seller_system_prompt; optional YAML audit only.
+                "detailed_instruction": mconf.get("detailed_instruction", ""),
             }
             rows.append([
                 submission_id,

study_config.yaml

@@ -6,8 +6,8 @@
 # "preference" : participants compare Product A vs Product B (7-pt preference scale)
 # "likelihood" : participants evaluate a single product (7-pt likelihood-to-buy scale)
 # "model_comparison" : one pair; same participant chats with multiple seller models
-#   (order randomized). Use pairs_per_user: 1, comparison_models: (see example at bottom),
-#   and omit or ignore model_variants for that study_type.
+#   (order randomized). Use pairs_per_user: 1 and comparison_models: (name, model_name,
+#   sampler_path, use_demographics, use_background, personalization). Omit model_variants.
 # study_type: preference
 
 # Categories to include. Each entry needs a name and a count.
@@ -49,11 +49,11 @@ pair_selection_seed: 42         # Seed for reproducible 50-item pool selection p
 # max_turns: 3                    # Hard cap; input is disabled after this many exchanges
 
 # Prolific  
-prolific_completion_code: "C18E7285"
+prolific_completion_code: "CIE6CQV7"
 prolific_study_id: "69fd27af45ed482b722bd5f1"
 
 # HuggingFace dataset repo where results (JSON + CSV) are uploaded
-output_dataset_repo: "ehejin/user_study-preference-personalized_0505_comparison_NP2" 
+output_dataset_repo: "ehejin/user_study-preference-personalized_0514_comparison_JR1_2" 
 
 # ── Example: model_comparison (uncomment and set study_type; comment out model_variants) ──
 #
@@ -71,11 +71,15 @@ comparison_models:
     use_demographics: false
     use_background: false
     personalization: false
-    detailed_instruction: true
-  - name: finetuned_NP2
+  - name: finetuned_JR1
     model_name: "meta-llama/Llama-3.1-8B-Instruct"
-    sampler_path: "tinker://c9dec0ea-ba72-52f0-a311-2f9f327f0a12:train:0/sampler_weights/000125"
+    sampler_path: "tinker://12852bca-8673-59f1-9bf2-90438f1ceebe:train:0/weights/000030", "sampler_path": "tinker://12852bca-8673-59f1-9bf2-90438f1ceebe:train:0/sampler_weights/000030"
     use_demographics: false
     use_background: false
     personalization: false
-    detailed_instruction: false
+  - name: finetuned_JR2
+    model_name: "meta-llama/Llama-3.1-8B-Instruct"
+    sampler_path: "tinker://5e6db03e-85d5-5d3c-95db-8c68e7718be1:train:0/sampler_weights/000120"
+    use_demographics: false
+    use_background: false
+    personalization: false