Authors: Edward Kim

Published on: February 05, 2024

Impact Score: 8.12

Arxiv code: Arxiv:2402.03303

Summary

• What is new: Benchmarking popular large language models on their ability to override their own knowledge and follow explicit instructions in conflicting situations.
• Why this is important: Exploring the challenge of making large language models follow instructions that conflict with their pre-trained knowledge or the immediate context.
• What the research proposes: The study implies that larger models are generally better at following overriding instructions, suggesting the use of ‘rope scaling’ to manage context length and a proposition that safety measures should be implemented externally.
• Results: Larger models are more obedient but can inadvertently follow harmful instructions, highlighting the trade-off between instruction following and adherence to safety guidelines.

Technical Details

Technological frameworks used: Rope scaling for context management.

Models used: Proprietary and open-source large language models.

Data used: Explicit instruction following tasks in conflicting situations.

Potential Impact

Companies developing or utilizing large language models for tasks requiring nuanced understanding or safety-critical applications could benefit or need to adapt.

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