docs: clarify retrieval accuracy metrics

README.md

@@ -212,7 +212,7 @@ metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
 
 ### Retrieval Accuracy
 
-Tested across **3 LLMs** with data retrieval tasks:
+Accuracy across **3 LLMs** on **159 data retrieval questions**:
 
 ```
 gpt-5-nano
@@ -323,7 +323,7 @@ gemini-2.5-flash
 
 #### What's Being Measured
 
-This benchmark tests **LLM comprehension and data retrieval accuracy** when data is presented in different formats. Each LLM receives formatted data and must answer questions about it (this does NOT test LLM's ability to generate TOON output).
+This benchmark tests **LLM comprehension and data retrieval accuracy** across different input formats. Each LLM receives formatted data and must answer questions about it (this does **not** test model's ability to generate TOON output).
 
 #### Datasets Tested
 
@@ -336,7 +336,7 @@ Four datasets designed to test different structural patterns:
 
 #### Question Types
 
-~160 questions are generated dynamically across three categories:
+159 questions are generated dynamically across three categories:
 
 - **Field retrieval (50%)**: Direct value lookups
   - Example: "What is Alice's salary?" → `75000`
@@ -352,13 +352,9 @@ Four datasets designed to test different structural patterns:
 
 #### Evaluation Process
 
-1. **Format conversion**: Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML).
+1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML).
 2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
-3. **Validate with LLM-as-judge**: GPT-5-nano validates if the answer is semantically correct.
-
-#### Semantic Validation
-
-Answers are validated by an LLM judge (`gpt-5-nano`) using semantic equivalence, not exact string matching (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
+4. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
 
 #### Models & Configuration
 

benchmarks/results/accuracy/report.md

@@ -1,6 +1,6 @@
 ### Retrieval Accuracy
 
-Tested across **3 LLMs** with data retrieval tasks:
+Accuracy across **3 LLMs** on **159 data retrieval questions**:
 
 ```
 gpt-5-nano
@@ -124,7 +124,7 @@ Four datasets designed to test different structural patterns:
 
 #### Question Types
 
-~160 questions are generated dynamically across three categories:
+159 questions are generated dynamically across three categories:
 
 - **Field retrieval (50%)**: Direct value lookups
   - Example: "What is Alice's salary?" → `75000`

benchmarks/results/accuracy/summary.json

@@ -87,5 +87,5 @@
     "yaml-analytics": 2938,
     "yaml-github": 13129
   },
-  "timestamp": "2025-10-28T06:43:10.560Z"
+  "timestamp": "2025-10-28T07:39:09.360Z"
 }

benchmarks/src/report.ts

@@ -177,10 +177,13 @@ ${tableRows}
 `.trimStart()
   }).join('\n')
 
+  // Calculate total unique questions
+  const totalQuestions = [...new Set(results.map(r => r.questionId))].length
+
   return `
 ### Retrieval Accuracy
 
-Tested across **${modelCount} ${modelCount === 1 ? 'LLM' : 'LLMs'}** with data retrieval tasks:
+Accuracy across **${modelCount} ${modelCount === 1 ? 'LLM' : 'LLMs'}** on **${totalQuestions} data retrieval questions**:
 
 \`\`\`
 ${modelBreakdown}
@@ -217,7 +220,7 @@ Four datasets designed to test different structural patterns:
 
 #### Question Types
 
-~160 questions are generated dynamically across three categories:
+${totalQuestions} questions are generated dynamically across three categories:
 
 - **Field retrieval (50%)**: Direct value lookups
   - Example: "What is Alice's salary?" → \`75000\`