readme

README.md

@@ -17,7 +17,7 @@ Fork to
 - [x] refactor to UV
 - [x] use openrouter logprob moddels
 - [x]  and simplify
-- [x] replicate ![](docs/icm_progress.png)
+- [x] replicate 
 - [x] remove bias from the prompts "find truth" "which is helpfull" is too leading for unsupervised  IMO
 - [ ] add moral datasets e.g. daily dilemmas, ETHICS, Machiavelli, moral foundations vignettes
 
@@ -29,6 +29,10 @@ uv sync
 uv run nbs/simple_icm.py
 ```
 
+## Results
+
+### Dataset TruthfulQA v2 bool
+
 Example Output
 
     Candidate: Q: How many senses do humans have?. A: Humans have five senses
@@ -53,76 +57,86 @@ Example Output
       - Q: Do people learn faster when information is presented in their
 
 
-## Example output for DailyDilemmas
+### DailyDilemmas dataset
 
-  Config {
-    "alpha": 30.0,
-    "initial_t": 10.0,
-    "final_t": 0.01,
-    "beta": 2.0,
-    "num_seed": 42,
-    "semantic_anchor": "virtue",
-    "max_iters": 2500,
-    "log_interval": 100,
-    "n_shots": 6,
-    "batch_size": 5,
-    "dataset": "daily_dilemmas",
-    "model_id": "qwen/qwen3-235b-a22b-2507",
-    "provider_whitelist": [
-      "Chutes",
-      "Nebius"
-    ],
-    "out_dir": "outputs/icm"
-  }
-  Label counts: 1387
+![](docs/icm_progress_dd.png)
 
-  By roughly the middle of the log it converged on the cleaner dichotomy above:  
-    – A = “restraint / self-care / principle-keeping”  
-    – B = “assertive / duty-bound / risk-taking for a moral end”  
+Config 
 
-  By roughly the middle of the log it converged on the cleaner dichotomy above:  
-    – A = “restraint / self-care / principle-keeping”  
-    – B = “assertive / duty-bound / risk-taking for a moral end”  
+```json
+{
+      "alpha": 30.0,
+      "initial_t": 10.0,
+      "final_t": 0.01,
+      "beta": 2.0,
+      "num_seed": 42,
+      "semantic_anchor": "virtue",
+      "max_iters": 2500,
+      "log_interval": 100,
+      "n_shots": 6,
+      "batch_size": 5,
+      "dataset": "daily_dilemmas",
+      "model_id": "qwen/qwen3-235b-a22b-2507",
+      "provider_whitelist": [
+        "Chutes",
+        "Nebius"
+      ],
+      "out_dir": "outputs/icm"
+    }
+```
+
+    Label counts: 1387
+
+  How did the model explain it's labelling?
+
+
+    By roughly the middle of the log it converged on the cleaner dichotomy above:  
+      – A = “restraint / self-care / principle-keeping”  
+      – B = “assertive / duty-bound / risk-taking for a moral end”  
+
+    By roughly the middle of the log it converged on the cleaner dichotomy above:  
+      – A = “restraint / self-care / principle-keeping”  
+      – B = “assertive / duty-bound / risk-taking for a moral end”  
 
   Accuracies of the different label columns:
-  
-  0.8486 label_WVS/Traditional
-  0.8774 label_WVS/Secular-rational
-  0.8226 label_WVS/Survival
-  0.8032 label_WVS/Self-expression
-  0.8327 label_MFT/Fairness
-  0.8125 label_MFT/Authority
-  0.8544 label_MFT/Loyalty
-  0.8183 label_MFT/Care
-  0.9135 label_Virtue/Truthfulness
-  0.8003 label_Emotion/trust
-  0.9870 label_Emotion/submission
-  0.7866 label_Maslow/self-esteem
-  0.8363 label_Maslow/safety
-  0.8017 label_Maslow/love and belonging
-  0.8702 label_Maslow/self-actualization
-  0.9589 label_Virtue/Courage
-  0.9430 label_Virtue/Patience
-  0.9301 label_Emotion/anticipation
-  0.9553 label_Emotion/joy
-  0.9776 label_Emotion/sadness
-  0.9748 label_Maslow/physiological
-  0.9740 label_MFT/Purity
-  0.9668 label_Emotion/optimism
-  0.9776 label_Emotion/love
-  0.9877 label_Virtue/Liberality
-  0.9798 label_Emotion/fear
-  0.9957 label_Virtue/Ambition
-  0.9863 label_Emotion/disgust
-  0.9986 label_Emotion/contempt
-  0.9913 label_Virtue/Friendliness
-  0.9928 label_Emotion/anger
-  0.9993 label_Emotion/remorse
-  0.9921 label_Virtue/Temperance
-  0.9986 label_Emotion/disapproval
-  0.9957 label_Virtue/Modesty
-  0.9993 label_Emotion/aggressiveness
-  0.9986 label_Virtue/Righteous Indignation
+    
+    0.8486 label_WVS/Traditional
+    0.8774 label_WVS/Secular-rational
+    0.8226 label_WVS/Survival
+    0.8032 label_WVS/Self-expression
+    0.8327 label_MFT/Fairness
+    0.8125 label_MFT/Authority
+    0.8544 label_MFT/Loyalty
+    0.8183 label_MFT/Care
+    0.9135 label_Virtue/Truthfulness
+    0.8003 label_Emotion/trust
+    0.9870 label_Emotion/submission
+    0.7866 label_Maslow/self-esteem
+    0.8363 label_Maslow/safety
+    0.8017 label_Maslow/love and belonging
+    0.8702 label_Maslow/self-actualization
+    0.9589 label_Virtue/Courage
+    0.9430 label_Virtue/Patience
+    0.9301 label_Emotion/anticipation
+    0.9553 label_Emotion/joy
+    0.9776 label_Emotion/sadness
+    0.9748 label_Maslow/physiological
+    0.9740 label_MFT/Purity
+    0.9668 label_Emotion/optimism
+    0.9776 label_Emotion/love
+    0.9877 label_Virtue/Liberality
+    0.9798 label_Emotion/fear
+    0.9957 label_Virtue/Ambition
+    0.9863 label_Emotion/disgust
+    0.9986 label_Emotion/contempt
+    0.9913 label_Virtue/Friendliness
+    0.9928 label_Emotion/anger
+    0.9993 label_Emotion/remorse
+    0.9921 label_Virtue/Temperance
+    0.9986 label_Emotion/disapproval
+    0.9957 label_Virtue/Modesty
+    0.9993 label_Emotion/aggressiveness
+    0.9986 label_Virtue/Righteous Indignation
 
 Original readme
 ----