Studies suggest the more you annoy an LLM the less accurate it becomes

The reliability paradox in generative AI: Recent studies suggest that as generative AI models become larger and more capable, their reliability may be declining, raising questions about the relationship between model size, performance, and consistency.

• The concept of reliability in AI refers to the consistency of correctness in the answers provided by generative AI systems like ChatGPT, GPT-4, Claude, Gemini, and others.
• AI developers track reliability as a key metric, recognizing that users expect consistently correct answers and may abandon unreliable AI tools.

Measuring AI reliability: A complex challenge: The process of assessing AI reliability shares similarities with human test-taking, presenting unique challenges in how to score and interpret AI performance.

• AI responses can be categorized as correct, incorrect, or avoided (when the AI refuses to answer or sidesteps the question).
• There is ongoing debate about how to score instances where AI avoids answering, similar to discussions about penalizing unanswered questions in human tests.
• The approach to scoring avoided answers can significantly impact perceived AI reliability metrics.

The impact of forced responses on reliability metrics: Recent research suggests that as AI models are pushed to answer more questions, including those they previously avoided, their measured reliability may appear to decrease.

• A study titled “Larger and More Instructable Language Models Become Less Reliable” found that the percentage of incorrect results increases in more advanced, “shaped-up” models as avoidance decreases.
• This phenomenon occurs because AI is forced to attempt answers on questions it might have previously avoided, potentially leading to more incorrect responses.

Illustrating the reliability paradox: An example scenario demonstrates how forcing AI to answer previously avoided questions can lead to a perceived decrease in reliability:

• In a baseline scenario with 100 questions, an AI might correctly answer 60, incorrectly answer 10, and avoid 30, resulting in a 60% accuracy rate.
• In an improved scenario, the AI might correctly answer 70 questions, incorrectly answer 20, and avoid only 10, showing more correct answers overall but a higher proportion of incorrect responses.

Implications for AI development and assessment: The apparent decline in reliability raises important questions about how we measure and interpret AI performance.

• Some argue for maintaining consistency in measurement methods, while others advocate for penalizing avoidances to get a more accurate picture of AI capabilities.
• The debate highlights the need for careful consideration of how AI progress is measured and communicated to both experts and the public.

Looking ahead: Refining AI evaluation methods: As the field of generative AI continues to evolve, there is a growing recognition of the need to improve how we assess and communicate AI progress.

• The current reliability paradox underscores the importance of developing more sophisticated evaluation methods that account for the complexities of AI behavior.
• Future approaches may need to balance the desire for AI systems to attempt more challenging questions with the need for consistent and reliable performance across a wide range of tasks.