Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGender and Representation Bias in GPT-3 Generated Stories
انحياز الجنس والتمثيل في القصص GPT-3 الناتجة
276
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
باستخدام شكل النمذجة ومشابه الكلمة القائمة على المعجم، نجد أن القصص الناتجة عن GPT-3 تظهر العديد من الصور النمطية الجنسانية المعروفة.قصص تم إنشاؤها تصور مواضيع وأوصاف مختلفة اعتمادا على الجنس المتصور GPT-3 من الشخصية في موجه، مع أحرف أنثوية أكثر عرضة للأسرة والمظهر، ووصف بأنه أقل قوة من أحرف الذكورية، حتى عند الاقتراح بأفعال عالية الطاقةفي موجه.تجمع دراستنا أسئلة حول كيف يمكن للمرء تجنب التحيزات الاجتماعية غير المقصودة عند استخدام نماذج لغة كبيرة لرواية القصص.
Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3's perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.
References used
https://aclanthology.org/
rate research
Read More
Human innovation in language, such as inventing new words, is a challenge for pretrained language models. We assess the ability of one large model, GPT-3, to process new words and decide on their meaning. We create a set of nonce words and prompt GPT
-3 to generate their dictionary definitions. We find GPT-3 produces plausible definitions that align with human judgments. Moreover, GPT-3's definitions are sometimes preferred to those invented by humans, signaling its intriguing ability not just to adapt, but to add to the evolving vocabulary of the English language.
This study tries to show the importance of the story title and its
significance in children's stories. It displays the sense of the subject and
point to the resources of titles of the children's stories, through critical
studying of groups of children's stories in the Arab world.
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attenti
on Networks in such a way to allow causal, sequential prediction of slot values. The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction. We report improvements in state tracking performance in MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level. We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.
AbstractMachine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a rela
tively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.
Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources o
f gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce auxiliary attributes' to provide tunable explanation complexity.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
