New content

content.py

@@ -9,10 +9,12 @@ GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with aug
 ## Context
 GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. GAIA data can be found in this space (https://huggingface.co/datasets/gaia-benchmark/GAIA). Questions are contained in `metadata.jsonl`. Some questions come with an additional file, that can be found in the same folder and whose id is given in the field `file_name`.
 
-It is divided in 3 levels, where level 1 should be breakable by very good LLMs, and level 3 indicate a strong jump in model capabilities, each divided into two sets: a fully public dev set, on which people can test their models, and a test set with private answers and metadata. Scores are expressed as the percentage of correct answers for a given split. 
+It is divided in 3 levels, where level 1 should be breakable by very good LLMs, and level 3 indicate a strong jump in model capabilities, each divided into a fully public dev set for validation, and a test set with private answers and metadata. 
+Results can be submitted for both validation and test. Scores are expressed as the percentage of correct answers for a given split. 
+
 
 ## Submissions
-Results can be submitted for both validation and test. We expect submissions to be json-line files with the following format. The first two fields are mandatory, `reasoning_trace` is optionnal:
+We expect submissions to be json-line files with the following format. The first two fields are mandatory, `reasoning_trace` is optionnal:
 ```
 {"task_id": "task_id_1", "model_answer": "Answer 1 from your model", "reasoning_trace": "The different steps by which your model reached answer 1"}
 {"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}