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README.md

@@ -33,17 +33,27 @@ dataset_info:
     dtype: string
   - name: power_relation
     dtype: string
-  - name: social_context
-    dtype: string
   - name: gold_standard
     dtype: string
+  - name: ann1_emotion
+    dtype: string
+  - name: ann2_emotion
+    dtype: string
+  - name: ann3_emotion
+    dtype: string
+  - name: valence_mean
+    dtype: float64
+  - name: arousal_mean
+    dtype: float64
+  - name: dominance_mean
+    dtype: float64
   splits:
   - name: train
-    num_examples: 210
+    num_examples: 211
   - name: validation
-    num_examples: 45
+    num_examples: 48
   - name: test
-    num_examples: 45
+    num_examples: 41
 ---
 
 # CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models
@@ -62,7 +72,26 @@ CEI (Contextual Emotional Inference) is a benchmark of 300 expert-authored scena
 ### Scenarios
 - **300 scenarios** across 5 pragmatic subtypes (60 each)
 - **3 independent annotations** per scenario (900 total)
-- **Predefined splits:** train (210), validation (45), test (45), stratified by subtype and power relation
+- **Predefined splits:** train (211), validation (48), test (41), stratified by subtype and power relation
+
+### Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | int | Scenario ID (unique within subtype) |
+| `subtype` | string | Pragmatic subtype (sarcasm-irony, mixed-signals, strategic-politeness, passive-aggression, deflection-misdirection) |
+| `context` | string | Situational context (2-4 sentences) |
+| `speaker` | string | Speaker's role in the scenario |
+| `listener` | string | Listener's role in the scenario |
+| `utterance` | string | The speaker's pragmatically ambiguous utterance |
+| `power_relation` | string | Power dynamic: peer, high-to-low, or low-to-high |
+| `gold_standard` | string | Gold-standard emotion (majority vote + expert adjudication) |
+| `ann1_emotion` | string | Annotator 1's emotion label (Plutchik) |
+| `ann2_emotion` | string | Annotator 2's emotion label (Plutchik) |
+| `ann3_emotion` | string | Annotator 3's emotion label (Plutchik) |
+| `valence_mean` | float | Mean valence rating across annotators (-1.0 to +1.0) |
+| `arousal_mean` | float | Mean arousal rating across annotators (-1.0 to +1.0) |
+| `dominance_mean` | float | Mean dominance rating across annotators (-1.0 to +1.0) |
 
 ### Pragmatic Subtypes
 | Subtype | Description | Fleiss' kappa |
@@ -75,9 +104,8 @@ CEI (Contextual Emotional Inference) is a benchmark of 300 expert-authored scena
 
 ### Labels
 - **Primary emotion:** One of Plutchik's 8 basic emotions (joy, trust, fear, surprise, sadness, disgust, anger, anticipation)
-- **VAD ratings:** Valence, Arousal, Dominance on 7-point scales mapped to [-1.0, +1.0]
-- **Confidence:** Annotator self-reported confidence
-- **Gold standard:** Majority vote with expert adjudication
+- **VAD ratings:** Mean Valence, Arousal, Dominance across 3 annotators, mapped to [-1.0, +1.0]
+- **Gold standard:** Majority vote with expert adjudication for three-way splits
 
 ### Power Relations
 - Peer (72%), High-to-Low authority (20%), Low-to-High authority (7%)
@@ -85,7 +113,7 @@ CEI (Contextual Emotional Inference) is a benchmark of 300 expert-authored scena
 ## Key Statistics
 - **Inter-annotator agreement:** Overall kappa = 0.21 (fair), ranging from 0.06 (deflection) to 0.25 (sarcasm)
 - **Human accuracy (vs. gold):** 61% mean, 14.3% unanimous, 31.3% three-way split
-- **Best LLM baseline:** 25.7% accuracy (Phi-4, zero-shot) vs. 54% human majority agreement
+- **Best LLM baseline:** 25.0% accuracy (Llama-3.1-70B, zero-shot) vs. 54% human majority agreement
 - **Random baseline:** 12.5% (8-class)
 
 ## Intended Uses
@@ -104,7 +132,7 @@ CEI (Contextual Emotional Inference) is a benchmark of 300 expert-authored scena
 ```bibtex
 @article{chun2026cei,
   title={CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models},
-  author={Chun, Jon and Sussman, Hannah and Mangine, Adrian and Kocaman, Murathan and Sidorko, Kirill and Koirala, Abhigya and McCloud, Andre and Eisenbeis, Gwen and Akanwe, Wisdom and Gassama, Moustapha and Gonzalez Chirinos, Eliezer and Enright, Anne-Duncan and Dunson, Peter and Ng, Tiffanie and von Rosenstiel, Anna and Idowu, Godwin},
+  author={Chun, Jon and Sussman, Hannah and Pechon-Elkins, Mateo and Mangine, Adrian and Kocaman, Murathan and Sidorko, Kirill and Koirala, Abhigya and McCloud, Andre and Eisenbeis, Gwen and Akanwe, Wisdom and Gassama, Moustapha and Gonzalez Chirinos, Eliezer and Enright, Anne-Duncan and Dunson, Peter and Ng, Tiffanie and von Rosenstiel, Anna and Idowu, Godwin},
   journal={Journal of Data-centric Machine Learning Research (DMLR)},
   year={2026}
 }