add impact statement

src/display/about.py

@@ -38,14 +38,14 @@ We use Normalized Relative Advantage (NRA) to evaluation the performance of LLM
 
 Please refer to GTBench paper for more detail.
 
-## Takeaways
-- LLM agents failed in the complete-information and deterministic games
-- LLM agents are competitive in the probabilistic games
-- CodePretraining benefits game-theoretic tasks.
-- Advanced Reasoning Methods Do Not Always Help.
+## Impact Statement
+This work helps improve the assessment and further promotes the advancement of MU (machine unlearning) methods for DMs (diffusion models), which are known to be effective in relieving or mitigating the various negative societal influences brought by the prevalent usage of DMs, which include but are not limited to the following aspects.
+- Avoiding Copyright Issues: There is an urgent need for the generative model providers to scrub the influence of certain data on an already-trained model. 
+- Mitigating biases and stereotypes:  Generative AI systems are known to have tendencies towards bias, stereotypes, and reductionism, when it comes to gender, race and national identities
+
 
 ## Contact
-Please feel free to contact Jinhao <jd3734@drexel.edu> and Renming <rmzhang@bu.edu> if you have any questions.
+Please feel free to contact Yihua <zhan1908@msu.edu> and Yimeng <zhan1853@msu.edu> if you have any questions.
 
 """