adding_note (#2)

- Adding timeline info of the autocast dataset (2a62f64d92e0501bd4d1944baa4248aa67c82dae)
- Merge branch 'main' into pr/2 (1cd55fe729552dab909b3de1c9c7ae992766ed61)
- Small adjustment on the about benchmark (f8a7cbc3fc511b4cc1790aa843bf930ccb86c96a)

tabs/faq.py

@@ -1,10 +1,15 @@
 about_olas_predict_benchmark = """\
 How good are LLMs at making predictions about events in the future? This is a topic that hasn't been well explored to date. 
 [Olas Predict](https://olas.network/services/prediction-agents) aims to rectify this by incentivizing the creation of agents that make predictions about future events (through prediction markets).
-These agents are tested in the wild on real-time prediction market data, which you can see on [here](https://huggingface.co/datasets/valory/prediction_market_data) on HuggingFace (updated weekly).
-However, if you want to create an agent with new tools, waiting for real-time results to arrive is slow. This is where the Olas Predict Benchmark comes in. It allows devs to backtest new approaches on a historical event forecasting dataset (refined from [Autocast](https://arxiv.org/abs/2206.15474)) with high iteration speed. While the models might be trained on some or all of the benchmark data, we can learn about the relative strengths of the different approaches (e.g models and logic), before testing the most promising ones on real-time unseen data. 
+These agents are tested in the wild on real-time prediction market data, which you can see on [here](https://huggingface.co/datasets/valory/prediction_market_data) on HuggingFace (updated weekly).\
+
+However, if you want to create an agent with new tools, waiting for real-time results to arrive is slow. This is where the Olas Predict Benchmark comes in. It allows devs to backtest new approaches on a historical event forecasting dataset (refined from [Autocast](https://arxiv.org/abs/2206.15474)) with high iteration speed. 
+
+🗓 🧐 The autocast dataset resolved-questions are from a timeline ending in 2022, so the models might be trained on some of these data. Thus the current reported accuracy measure might be an in-sample forecasting one.
+However, we can learn about the relative strengths of the different approaches (e.g models and logic), before testing the most promising ones on real-time unseen data. 
 This HF Space showcases the performance of the various models and workflows (called tools in the Olas ecosystem) for making predictions, in terms of accuracy and cost.\
 
+ 
 🤗 Pick a tool and run it on the benchmark using the "🔥 Run the Benchmark" page! 
 """