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Published Sep 9, 2021

Emily M. Bender — Language Models and Linguistics

Emily M. Bender delves into the intricacies of language models, discussing their limitations, biases, and ethical challenges while stressing the importance of more inclusive benchmarks, environmental responsibility, and ethical implementation strategies to harness their potential responsibly.
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Episode Highlights

  • Future Potential

    Emily M. Bender discusses the future potential of language models, emphasizing the need for accountability and environmental considerations. She suggests that language models should not be central to applications requiring reliable communication, as they are prone to errors and biases 1. Instead, Emily advocates for algorithms that can work with smaller data sets, which would allow for better curation and documentation, reducing reliance on large models 1. She also highlights the importance of measuring the environmental impact of these models, suggesting that leaner solutions could be more sustainable and flexible 1.

    Language models are going to remain useful, but we need a more stringent sense of what works and what's an appropriate range of failure modes.

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    Emily also calls for more rigorous testing and documentation of AI models, similar to practices in other engineering fields. She points out that the current culture in AI often prioritizes hype over reliability, which can stifle creativity and exclude smaller research groups 2.

       

    Current Uses

    The current uses of language models span various industries, with generative AI being a significant focus. These models are employed in business for tasks like content creation, customer service, and data analysis, showcasing their versatility and efficiency 3. However, Emily M. Bender cautions against over-reliance on these models, as they can perpetuate biases and inaccuracies if not properly managed 4. She stresses the importance of understanding the limitations of language models and ensuring they are used responsibly.

    It's not enough that GPT-3 can produce coherent text; people have to say it's understanding language, which it absolutely isn't.

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    Real-world applications demonstrate the potential of language models, but also highlight the need for careful implementation and oversight. Emily advocates for a balanced approach, where the benefits of AI are harnessed without compromising ethical standards or excluding smaller players in the field 4.

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