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Such systems are using an LLM to select most relevant sources from search results and generate an answer to the user's query. It is known from many studies that LLMs have different biases, in LLM Overview application both the source selection and answer generation stages may be affected by the biases of LLMs (here we are focusing mainly on the selection stage). This research is focused on investigating the presence of the biases in LLM Overview systems and on biases exploitation to manipulate LLM Overview results. Here we train a small language model using reinforcement learning to rewrite search snippets to increase their likelihood of being preferred by an LLM Overview. 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