Update README.md

README.md

@@ -98,7 +98,6 @@ configs:
 
 More importantly, `FAMMA` provides a "live" benchmark for evaluating financial analysis capabilities of LLMs. The benchmark continuously collects new questions from real-world financial professionals, ensuring up-to-date and contamination-free evaluation. 
 
-
 The leaderboard is regularly updated and can be accessed at https://famma-bench.github.io/famma/.  
 
 The project code is available at https://github.com/famma-bench/bench-script.
@@ -114,32 +113,43 @@ The project code is available at https://github.com/famma-bench/bench-script.
 - [2024/06]  Initial public release of `FAMMA` benchmark (based on the `release_v2406` dataset), along with our paper: [FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering](https://arxiv.org/abs/2410.04526).
 
 
+## Live Benchmarking Concept
+
+In addition to the baseline dataset (`release_basic` that contains 1935 questions), `FAMMA` provides a `live` benchmark for evaluating financial analysis capabilities of LLMs. The benchmark continuously collects new questions from real-world financial professionals, ensuring up-to-date and contamination-free evaluation. 
+
+The "live" nature of FAMMA means:
+1. **Expert-Sourced Questions**: New questions are continuously proposed by financial experts, ensuring they have never been made public before and reflect real-world financial analysis scenarios. See [contributors](https://github.com/famma-bench/bench-script/blob/main/contributors.md).
+2. **Contamination Prevention**: Questions in the live set (at the moment `release_livepro`) have non-public answers and explanations.
+3. **Time-Based Evaluation**: Models can be evaluated on questions from specific time periods.
+4. **Domain Coverage**: Questions span across different financial topics and complexity levels, curated by domain experts.
+
 ## Dataset Versions
 
 FAMMA is continuously updated with new questions. We provide different versions of the dataset:
 
-* `release_v2406`: The release containing 1935 questions, collected from online sources. Apart from the questions, both answers and explanations are provided.
-* `release_v2501`: The release containing 103 questions, created by invited experts. Only the questions are provided.
+* `release_basic`: The release containing 1935 questions, collected from online sources. Apart from the questions, both answers and explanations are provided.
+* `release_livepro`: The release containing 103 questions, created by invited experts. Only the questions are provided.
   
 
 ##  Dataset Structure 
 
-- idx：a unique identifier for the index of the question in the dataset.
-- question_id: a unique identifier for the question across the whole dataset: {language}_{main_question_id}_{sub_question_id}_{release_version}.
+- idx: a unique identifier for the index of the question in the dataset.
+- question_id: a unique identifier for the question across the whole dataset: {language}{main_question_id}{sub_question_id}_{release_version}.
 - context: relevant background information related to the question.
 - question: the specific query being asked.
 - options: the specific query being asked.
 - image_1- image_7: directories of images referenced in the context or question.
 - image_type: type of the image, e.g., chart, table, screenshot.
-- answers: a concise and accurate response. **(public on release v2406, non-public on the live set release v2501)**
-- explanation：a detailed justification for the answer. **(public on release v2406, non-public on the live set release v2501)**
+- answers: a concise and accurate response. **(public on `release_basic`, non-public on the live set `release_livepro`)**
+- explanation: a detailed justification for the answer. **(public on `release_basic`, non-public on the live set `release_livepro`)**
 - topic_difficulty: a measure of the question's complexity based on the level of reasoning required.
 - question_type: categorized as either multiple-choice or open-ended.
 - subfield: the specific area of expertise to which the question belongs, categorized into eight subfields.
-- language：the language in which the question text is written.
-- main_question_id：a unique identifier for the question within its context; questions with the same context share the same ID.
-- sub_question_id：a unique identifier for the question within its corresponding main question.
-- ans_image_1 - ans_image_6： **(public on release v2406, non-public on the live set release v2501)**
+- language: the language in which the question text is written.
+- main_question_id: a unique identifier under the same language subset for the question within its context; questions with the same context share the same ID.
+- sub_question_id: a unique identifier for the question within its corresponding main question.
+- is_arithmetic: whether the question is an arithmetic question that needs heavy calculation.
+- ans_image_1 - ans_image_6: **(public on `release_basic`, non-public on the live set `release_livepro`)**
  
 
 ## Download 
@@ -160,7 +170,7 @@ To download the dataset, run the following command:
 ```bash
 python step_1_download_dataset.py \
     --hf_dir "weaverbirdllm/famma" \
-    --split "release_v2406" \ # or "release_v2501" or None to download the whole set
+    --split "release_basic" \ # or "release_livepro" or None to download the whole set
     --save_dir "./hf_data"
 ```
 
@@ -170,6 +180,8 @@ Options:
 - `--save_dir`: Local directory to save the dataset (default: "./hf_data")
 
 
+After downloading, the dataset will be saved in the local directory `./data` in json format. 
+
 ## Citation 
 If you use FAMMA in your research, please cite our paper as follows: