Update README.md

README.md

@@ -72,7 +72,7 @@ The proposed Q-Bench includes three realms for low-level vision: perception (A1)
 - For assessment (A3), as we use **public datasets**, we provide an abstract evaluation code for arbitrary MLLMs for anyone to test.
 
 ## Release
-- [2/10] 🔥 We are releasing the extended [Q-bench+](https://github.com/Q-Future/Q-Bench/blob/master/Q_Bench%2B.pdf), which challenges MLLMs with both single images and image pairs on low-level vision. The [LeaderBoard](https://huggingface.co/spaces/q-future/Q-Bench-Leaderboard) is onsite, check out the low-level vision ability for your favourite MLLMs!! More details coming soon!
+- [2/10] 🔥 We are releasing the extended [Q-bench+](https://github.com/Q-Future/Q-Bench/blob/master/Q_Bench%2B.pdf), which challenges MLLMs with both single images and image pairs on low-level vision. The [LeaderBoard](https://huggingface.co/spaces/q-future/Q-Bench-Leaderboard) is onsite, check out the low-level vision ability for your favorite MLLMs!! More details coming soon.
 - [1/16] 🔥 Our work ["Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision"](https://arxiv.org/abs/2309.14181) is accepted by **ICLR2024 as Spotlight Presentation**.
 
 ## Close-source MLLMs (GPT-4V-Turbo, Gemini, Qwen-VL-Plus, GPT-4V)