--The paper explores the study of gender-based narrative biases in stories generated by ChatGPT, Gemini, and Claude. The prompt design draws on Propp's character classifications and Freytag's narrative structure. The stories are analyzed through a close reading approach, with particular attention to adherence to the prompt, gender distribution of characters, physical and psychological descriptions, actions, and finally, plot development and character relationships. The results reveal the persistence of biases -- especially implicit ones -- in the generated stories and highlight the importance of assessing biases at multiple levels using an interpretative approach. In recent years, considerable attention has been paid to addressing the problem of bias in Large Language Models (LLMs). Despite ongoing efforts and improvements, LLMs still often do not adequately represent diversity and continue to propagate various forms of societal bias in their output [1] [2] [3]. The extensive use of LLMs for content creation and text generation makes this issue increasingly urgent. Regarding gender bias, studies have explored different aspects, such as the correlation between gender and occupation [4] [5], personas [6] [7], or the use of adjectives [8]. Many of these studies also compared LLMs' correlations with official social data on occupation and human perceptions [5] [9]. Methodologies for studying bias in LLMs can be divided into intrinsic and extrinsic approaches [10] [11]. The intrinsic approach includes embedding-and probability-based bias, while the extrinsic approach focuses on generation-based bias [12]. A recent study from UNESCO [13] provides a comprehensive application of various approaches by studying the connection of gendered words, asking LLMs to complete sentences, and generating entire stories. There are different modes of gender bias and stereotype propagation, and it is important to evaluate the issue from various points of view.

In this work, we present the first application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model in the context of emotion recognition, a task where both visual and audio modalities play crucial roles. We leverage RLVR to optimize the Omni model, significantly enhancing its performance in three key aspects: reasoning capability, emotion recognition accuracy, and generalization ability. The introduction of RLVR not only improves the model's overall performance on in-distribution data but also demonstrates superior robustness when evaluated on out-of-distribution datasets. More importantly, the improved reasoning capability enables clear analysis of the contributions of different modalities, particularly visual and audio information, in the emotion recognition process. This provides valuable insights into the optimization of multimodal large language models.

Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literature analyzing such biases in children stories. Because these analyses are often conducted manually and at a small scale, such investigations can benefit from the use of more recent natural language processing methods that examine social bias in models and data corpora. Our work joins this interdisciplinary effort and makes a unique contribution by taking into account the event narrative structures when analyzing the social bias of stories. We propose a computational pipeline that automatically extracts a story's temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. We also present a verb-based event annotation scheme that can facilitate bias analysis by including categories such as those that align with traditional stereotypes. Through a case study analyzing gender bias in fairy tales, we demonstrate that our framework can reveal bias in not only the unigram verb-based events in which female and male characters participate but also in the temporal narrative order of such event participation.

Characters in books are about four times more likely to be male than female, a new study of gender bias in literature has revealed. Researchers at the USC Viterbi School of Engineering used artificial intelligence to examine more than 3,000 English-language books ranging from science fiction and adventure, to mystery and romance - across short stories, poetry and novels. The team found male characters appeared four times as often as females across the books, although that reduced when the author of the work was female. There were also more negative terms used in connection with the female characters such as'weak' and'stupid' compared to'strong' and'power' used for men. 'Gender bias is real, and when we see females four times less in literature, it has a subliminal impact on people consuming the culture,' said author Mayank Kejriwal.