içgörü - Natural Language Processing - # Representativeness and Bias in Generative AI for Multilingual and Low Resource Languages

Linguistic Differences Between Written Genres of Nigerian Pidgin: Implications for Representativeness in Generative AI

Q: What are the potential societal implications of bias towards certain genres or registers in Generative AI systems for low-resource and multilingual languages?

Bias towards certain genres or registers in Generative AI systems for low-resource and multilingual languages can have significant societal implications: Marginalization of Underrepresented Groups: If AI models are biased towards specific genres or registers that are associated with privileged or dominant groups, it can marginalize speakers of other genres or registers, particularly those from marginalized communities. This can perpetuate existing power dynamics and inequalities within society. Limited Access to Information: Bias towards certain genres may result in limited access to information or services for speakers of other genres. For example, if AI systems predominantly understand formal registers but struggle with informal or regional dialects, it can hinder communication and access to resources for speakers of those dialects. Cultural Preservation: Certain genres or registers may be more closely tied to cultural heritage or identity. Bias towards mainstream genres could lead to the erasure of cultural nuances and linguistic diversity within a language, impacting cultural preservation efforts. Inaccurate Representations: If AI models are biased towards specific genres, they may produce inaccurate or unnatural outputs when tasked with generating content in other genres. This can lead to misunderstandings, miscommunications, and loss of meaning in interactions involving different genres or registers. Reinforcement of Stereotypes: Bias towards certain genres or registers can reinforce stereotypes or stigmas associated with particular linguistic styles. This can perpetuate discrimination and prejudice against speakers of those genres, further exacerbating social divides. Addressing bias in Generative AI systems for low-resource and multilingual languages is essential to promote linguistic diversity, inclusivity, and equitable access to technology for all members of society.

Q: How can we leverage the linguistic differences between genres to improve the performance and inclusiveness of Generative AI models for diverse user groups?

Leveraging the linguistic differences between genres can enhance the performance and inclusiveness of Generative AI models in the following ways: Genre-specific Training: By training AI models on diverse datasets that include samples from different genres, the models can learn to adapt to the unique linguistic features of each genre. This exposure helps the models generate more accurate and contextually appropriate outputs for diverse user groups. Fine-tuning for Genre Adaptation: Fine-tuning AI models on genre-specific data can improve their ability to generate content that aligns with the linguistic norms and conventions of each genre. This fine-tuning process allows the models to capture genre-specific nuances and produce more tailored outputs. Prompting and Few-shot Learning: Utilizing prompting techniques and few-shot learning approaches can help AI models quickly adapt to new genres or registers with minimal examples. By providing targeted prompts for different genres, the models can learn to generate content that resonates with diverse user groups. Evaluation and Feedback Loop: Implementing an evaluation and feedback loop system where users can provide feedback on the generated outputs can help AI models refine their understanding of different genres. This iterative process allows the models to continuously improve their performance and inclusiveness over time. Collaboration with Linguistic Experts: Collaborating with linguistic experts, language scholars, and native speakers representing various genres can provide valuable insights into the nuances of different linguistic styles. This collaboration can inform the development of AI models that are more sensitive to genre-specific variations. By leveraging the linguistic differences between genres, Generative AI models can be optimized to cater to diverse user groups, enhance linguistic inclusivity, and improve the overall quality of generated content across various genres and registers.

Temel Kavramlar

The two written genres of Nigerian Pidgin (BBC and Wikipedia) are linguistically distinct and do not represent each other, leading to bias in Generative AI models that are predominantly trained on the BBC genre.

Özet

The paper introduces the WARRI dataset, which contains parallel sentences in English and the two written genres of Nigerian Pidgin (BBC and Wikipedia). Through statistical analysis and machine translation experiments, the authors demonstrate that the BBC and Wikipedia genres of Nigerian Pidgin are significantly different in terms of word order, vocabulary, and linguistic features.

The BBC genre is closer to English and favored by more educated Nigerians, while the Wikipedia genre is closer to the spoken form of Nigerian Pidgin and more accessible to a wider range of speakers. The authors find that current Generative AI models, such as GPT-4-TURBO and LLAMA 2 13B, are biased towards the BBC genre and do not adequately represent the Wikipedia genre. This bias can lead to exclusion of certain communities of Nigerian Pidgin speakers.

The paper highlights the importance of ensuring representativeness of linguistic variation in the data used to train Generative AI systems, especially for multilingual and low-resource languages like Nigerian Pidgin. The authors recommend that the different genres of a language should be integrated into the systems separately to achieve better inclusivity and accessibility for diverse user groups.

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arxiv.org

İstatistikler

The BBC genre of Nigerian Pidgin has a unigram Jaccard similarity of 0.712-0.802 with its parallel English corpus, while the Wikipedia genre has a lower similarity of 0.517.
The Levenshtein distance between the English sentences and their translations in the Wikipedia genre is more than twice the distance for the BBC genre, indicating greater linguistic differences.
The MAFAND MT model fine-tuned on the BBC genre achieves 83.4 ChrF++ on the BBC test set but only 59.1 ChrF++ on the Wikipedia test set.
GPT-4-TURBO and LLAMA 2 13B perform better on the BBC genre compared to the Wikipedia genre, even with few-shot prompting.

Alıntılar

"Di name "ABIA" na from di first letter of di four places wey get plenty people for di state."
"Di name "ABIA" come from di first letter of di four places wey people plenty well well for di state."

Önemli Bilgiler Şuradan Elde Edildi

Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages

by Davi... : arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19442.pdf

Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages

Daha Derin Sorular

How can we develop Generative AI models that are truly representative of the linguistic diversity within a language, including different genres and registers?

Developing Generative AI models that accurately represent the linguistic diversity within a language, including different genres and registers, requires a multi-faceted approach. Here are some key strategies:

Diverse Training Data: To ensure representativeness, AI models should be trained on a diverse range of data sources that cover various genres and registers within the language. This includes incorporating data from different written genres, spoken language samples, formal and informal registers, and different dialects or variations of the language.

Genre-specific Fine-tuning: After pre-training on a diverse dataset, fine-tuning the AI models on specific genre-specific data can help them adapt to the nuances and characteristics of different genres. This fine-tuning process should involve a balanced mix of data from each genre to avoid bias towards any particular style.

Evaluation Metrics: Developing evaluation metrics that assess the model's performance across different genres and registers is crucial. Metrics should not only focus on traditional measures like BLEU scores but also consider genre-specific linguistic features, such as vocabulary choice, sentence structure, and tone.

Inclusive Dataset Creation: Collaborating with linguists, language experts, and native speakers from diverse backgrounds to create inclusive datasets that represent the full spectrum of linguistic diversity within the language. This ensures that the training data is comprehensive and reflective of the language's richness.

Continuous Learning and Adaptation: AI models should be designed to continuously learn and adapt to new linguistic patterns and variations. This can be achieved through active learning techniques, where the model interacts with users to improve its understanding of different genres and registers over time.

By implementing these strategies, Generative AI models can be developed to be more inclusive, accurate, and representative of the linguistic diversity within a language.

What are the potential societal implications of bias towards certain genres or registers in Generative AI systems for low-resource and multilingual languages?

Bias towards certain genres or registers in Generative AI systems for low-resource and multilingual languages can have significant societal implications:

Marginalization of Underrepresented Groups: If AI models are biased towards specific genres or registers that are associated with privileged or dominant groups, it can marginalize speakers of other genres or registers, particularly those from marginalized communities. This can perpetuate existing power dynamics and inequalities within society.

Limited Access to Information: Bias towards certain genres may result in limited access to information or services for speakers of other genres. For example, if AI systems predominantly understand formal registers but struggle with informal or regional dialects, it can hinder communication and access to resources for speakers of those dialects.

Cultural Preservation: Certain genres or registers may be more closely tied to cultural heritage or identity. Bias towards mainstream genres could lead to the erasure of cultural nuances and linguistic diversity within a language, impacting cultural preservation efforts.

Inaccurate Representations: If AI models are biased towards specific genres, they may produce inaccurate or unnatural outputs when tasked with generating content in other genres. This can lead to misunderstandings, miscommunications, and loss of meaning in interactions involving different genres or registers.

Reinforcement of Stereotypes: Bias towards certain genres or registers can reinforce stereotypes or stigmas associated with particular linguistic styles. This can perpetuate discrimination and prejudice against speakers of those genres, further exacerbating social divides.

Addressing bias in Generative AI systems for low-resource and multilingual languages is essential to promote linguistic diversity, inclusivity, and equitable access to technology for all members of society.

How can we leverage the linguistic differences between genres to improve the performance and inclusiveness of Generative AI models for diverse user groups?

Leveraging the linguistic differences between genres can enhance the performance and inclusiveness of Generative AI models in the following ways:

Genre-specific Training: By training AI models on diverse datasets that include samples from different genres, the models can learn to adapt to the unique linguistic features of each genre. This exposure helps the models generate more accurate and contextually appropriate outputs for diverse user groups.

Fine-tuning for Genre Adaptation: Fine-tuning AI models on genre-specific data can improve their ability to generate content that aligns with the linguistic norms and conventions of each genre. This fine-tuning process allows the models to capture genre-specific nuances and produce more tailored outputs.

Prompting and Few-shot Learning: Utilizing prompting techniques and few-shot learning approaches can help AI models quickly adapt to new genres or registers with minimal examples. By providing targeted prompts for different genres, the models can learn to generate content that resonates with diverse user groups.

Evaluation and Feedback Loop: Implementing an evaluation and feedback loop system where users can provide feedback on the generated outputs can help AI models refine their understanding of different genres. This iterative process allows the models to continuously improve their performance and inclusiveness over time.

Collaboration with Linguistic Experts: Collaborating with linguistic experts, language scholars, and native speakers representing various genres can provide valuable insights into the nuances of different linguistic styles. This collaboration can inform the development of AI models that are more sensitive to genre-specific variations.

By leveraging the linguistic differences between genres, Generative AI models can be optimized to cater to diverse user groups, enhance linguistic inclusivity, and improve the overall quality of generated content across various genres and registers.