The field of natural language processing is witnessing a significant shift towards mitigating biases in language models. Researchers are actively exploring innovative methods to reduce gender bias and improve the understanding of negation in vision language models. One of the key directions is the development of novel data generation frameworks that foster exploratory thinking in large language models, leading to more balanced and neutral judgments. Another area of focus is the creation of benchmarks to assess the ability of vision language models to comprehend negation, which has revealed critical gaps in their performance. Additionally, there is a growing interest in developing gender-neutral rewriting systems for languages other than English, which can help mitigate biases in textual data. Overall, these efforts aim to advance the field by promoting more inclusive and unbiased language models. Noteworthy papers include: NegVQA, which introduces a benchmark for evaluating the ability of vision language models to understand negation, and Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs, which proposes a novel data generation framework to reduce gender bias in large language models. GeNRe is also a significant contribution, presenting the first French gender-neutral rewriting system using collective nouns.