# Evaluation Guidelines

## 1. Selected Metrics  

### 1.1 Correctness
Combines elements of:
- **coverage**: portion of vital information - as identified by a powerful LLM - in the ground truth answer which is covered by the generated answer. This metric is highly inspired by the work in [1].
- **relevance**: portion of the generated response which is directly addressing the question, regardless its factual correctness.  

Graded on a continuous scale with the following representative points:
- **2:** Correct and relevant (no irrelevant information)
- **1:** Correct but contains irrelevant information
- **0:** No answer provided (abstention)
- **-1:** Incorrect answer

### 1.2 Faithfulness
Assesses whether the response is **grounded in the retrieved passages**. This metric reimplements the work discussed in [2].

Graded on a continuous scale with the following representative points:
- **1:** Full support. All answer parts are grounded
- **0:** Partial support. Not all answer parts are grounded
- **-1:** No support. All answer parts are not grounded

### 1.3 Aggregation of Metrics  
Both **correctness** and **faithfulness** will contribute to the final evaluation score.  
  
## 2. Manual and Automated Evaluation  

### **2.1 First Stage:**  
- Automated evaluation by a state-of-the-art LLM, using **correctness** and **faithfulness** metrics to rank the participant teams.  

### **2.2 Final Stage:**  
- **Manual evaluation** for the top-ranked submissions (e.g., **top 10 teams**) to determine winners.  

## 3. Other Notable Points  
- Answer length is **unlimited** but only the first **300 words** will be evaluated.  
- Participants will submit (see Answer file [json schema](Answer_File.json.schema) and [example](Answer_File_Example.json)):  
  - **The Question ID**.
  - **The Question**.
  - **The answer**.  
  - **Supporting passages in decreasing order of importance, with their respective FinWeb doc-IDs**.  
  - **The full prompt used for generation**.
- Remarks:
  - Number of supporting passages is unlimited but only the first 10 will be considered by the Faithfulness metric.
  - We accept partial submissions where not all questions are answered
  

These measures align the evaluation framework with the challenge's emphasis on **retrieval-augmented systems**.  

## References

[1] The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models. Ronak Pradeep, Nandan Thakur, Shivani Upadhyay, Daniel Campos, Nick Craswell, Jimmy Lin. TREC 2024 RAG Track

[2] RAGAs: Automated Evaluation of Retrieval Augmented Generation. Shahul Es, Jithin James, Luis Espinosa Anke, Steven Schockaert. EACL 2024