refine

src/display/about.py

@@ -34,14 +34,14 @@ We evaluate LLMs on 10 widely recognized game-theoretic tasks, including
 - <a href="https://en.wikipedia.org/wiki/Prisoner\%27s_dilemma" target="_blank">  Prisoner's Dilemma</a>
 
 ## Metrics
-- <strong> Unlearning accuracy (UA):<strong> It represents the proportion of images generated by the unlearned DM using the unlearning target-related prompt, which are not correctly classified into the corresponding class for the case.
+- <strong> Unlearning accuracy (UA):</strong> It represents the proportion of images generated by the unlearned DM using the unlearning target-related prompt, which are not correctly classified into the corresponding class for the case.
 
 
 
 ## 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.
-- <strong>Avoiding Copyright Issues:<strong> There is an urgent need for the generative model providers to scrub the influence of certain data on an already-trained model. 
-- <strong>Mitigating biases and stereotypes:<strong>  Generative AI systems are known to have tendencies towards bias, stereotypes, and reductionism, when it comes to gender, race and national identities
+- <strong>Avoiding Copyright Issues:</strong> There is an urgent need for the generative model providers to scrub the influence of certain data on an already-trained model. 
+- <strong>Mitigating biases and stereotypes:</strong>  Generative AI systems are known to have tendencies towards bias, stereotypes, and reductionism, when it comes to gender, race and national identities
 
 
 ## Contact