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@@ -362,13 +362,13 @@ <h2 class="title is-3">Abstract</h2>
                 <b>1.4K Fine-Grained and Diverse Human Preference Data</b>: We collect 1.4K pieces of segment-level corrections of human feedback on hallucinations, covering hallucination types including objects (41.2%), positions (20.3%), numbers (16.5%), attributes (10%), actions (5.3%), and others (6.8%).
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-                <b>High Data Efficiency and Scalability</b>: With just 1.4K annotated data, we achieved 34.8% reduction in model hallucinations. Moreover, the decrease in hallucinations becomes more significant as more data is used.
+                <b>High Data Efficiency and Scalability</b>: With just 1.4K annotated data, we achieved a 34.8% reduction in model hallucinations. Moreover, the decrease in hallucinations becomes more significant as more data used.
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                 <b>Enhanced Performance and Computational Efficiency with DDPO</b>: Our Dense Direct Preference Optimization (DDPO) algorithm can better exploit the fine-grained human feedback, allowing training in under 1 hour on 8 A100 GPUs.
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-                <b>Outstanding Trustworthiness without Compromising Helpfulness</b>: Our model surpasses existing open-source MLLMs in reducing hallucinations rates, mitigates hallucination from over-generalization, and maintains informativeness.
+                <b>Outstanding Trustworthiness without Compromising Helpfulness</b>: Our model surpasses existing open-source MLLMs in reducing hallucination rates, mitigates hallucination from over-generalization, and maintains informativeness.
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