On the dangers of stochastic parrots: Can language models be too big? 🦜

TL;DR

Large language models pose environmental and financial costs, perpetuate biases, lack specificity, and have potential for harmful synthetic language generation.

Transcript

hello everyone welcome uh we're really excited today to have a wonderful uh set of people to come and talk about a very important uh set of topics um i'll just briefly describe the format um to make sure everyone knows what's going on we're gonna open up with emily bender uh who has graciously uh joined us to talk about her paper uh that co-authore... Read More

Key Insights

• ⬛ Large language models present environmental and financial costs due to their high energy consumption and data requirements.
• ❓ Unmanageable training data can result in biased models that perpetuate systems of oppression.
• 👨‍🔬 Research trajectories focused on generality and task performance may overlook meaningful language understanding.
• 🥺 Synthetic language generated by language models can lead to misinterpretation, misinformation, and harmful behavior.
• *️⃣ Risk management strategies include intentional data collection, documentation, and careful consideration of the societal impacts of large language models.

Questions & Answers

Q: How do large language models contribute to environmental and financial costs?

Large language models require extensive energy and compute resources for training, leading to significant environmental impact. Additionally, the costs associated with training and maintaining these models are substantial, making them financially burdensome.

Q: What are the risks associated with unmanageable training data?

Unmanageable training data can result in models that encode biases and perpetuate systems of oppression. The overrepresentation of hegemonic viewpoints in the data can lead to harmful language generation and contribute to discriminatory outcomes.

Q: What concerns are raised about research trajectories focused on generality and performance?

A focus on generality and performance in research trajectories may overlook the significance of meaningful language understanding. By prioritizing task performance over understanding and context, models may produce outputs that are inaccurate, misleading, or harmful.

Q: How does synthetic language generated by large language models impact human interpretation?

Synthetic language generated by language models can be misinterpreted by humans. Coherence is subjective, and humans have a tendency to ascribe meaning to synthetic text, even if it lacks intention or understanding. This can lead to the dissemination of misinformation and harmful behavior.

Q: What risk management strategies can mitigate the dangers of large language models?

Risk management strategies include intentional data collection, documentation, and analysis. By selecting datasets intentionally and documenting the process, researchers can identify biases and potential harms associated with large language models. Informed analyses and value-sensitive design can further contribute to mitigating risks and developing safer models.

Summary & Key Takeaways

• Emily Bender discusses the limitations and potential risks associated with large language models.

• She highlights the environmental and financial costs of training these models, as well as the biases and lack of specificity in the training data.

• Bender also raises concerns about the potential harm caused by synthetic language generation and the need for risk mitigation strategies.

Key Insights:

• Large language models present environmental and financial costs due to their high energy consumption and data requirements.

• Unmanageable training data can result in biased models that perpetuate systems of oppression.

• Research trajectories focused on generality and task performance may overlook meaningful language understanding.

• Synthetic language generated by language models can lead to misinterpretation, misinformation, and harmful behavior.

• Risk management strategies include intentional data collection, documentation, and careful consideration of the societal impacts of large language models.