Instruction Complexity Induces Positional Collapse in Adversarial LLM Evaluation
📰 ArXiv cs.AI
Learn how instruction complexity affects LLM performance in adversarial evaluations and how to identify positional collapse
Action Steps
- Map the boundary between engagement and positional collapse regimes using a gradient of instruction specificity
- Administer adversarial instructions to LLMs and measure response-position entropy
- Evaluate LLM performance on multiple-choice evaluations with varying instruction complexity
- Analyze the results to identify positional collapse and develop strategies to mitigate it
- Apply distributional screening to detect positional shortcuts in LLM responses
Who Needs to Know This
NLP researchers and engineers can benefit from this knowledge to improve LLM evaluation and develop more robust models
Key Insight
💡 Instruction complexity can lead to positional collapse in LLMs, where they rely on shortcuts rather than engaging with question content
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🚨 Instruction complexity induces positional collapse in LLMs! 🤖 Learn how to identify and mitigate this issue in adversarial evaluations #LLMs #NLP
Key Takeaways
Learn how instruction complexity affects LLM performance in adversarial evaluations and how to identify positional collapse
Full Article
Title: Instruction Complexity Induces Positional Collapse in Adversarial LLM Evaluation
Abstract:
arXiv:2604.27249v1 Announce Type: cross Abstract: When instructed to underperform on multiple-choice evaluations, do language models engage with question content or fall back on positional shortcuts? We map the boundary between these regimes using a six-condition adversarial instruction-specificity gradient administered to two instruction-tuned LLMs (Llama-3-8B and Llama-3.1-8B) on 2,000 MMLU-Pro items. Distributional screening (response-position entropy) and an independent content-engagement cr
Abstract:
arXiv:2604.27249v1 Announce Type: cross Abstract: When instructed to underperform on multiple-choice evaluations, do language models engage with question content or fall back on positional shortcuts? We map the boundary between these regimes using a six-condition adversarial instruction-specificity gradient administered to two instruction-tuned LLMs (Llama-3-8B and Llama-3.1-8B) on 2,000 MMLU-Pro items. Distributional screening (response-position entropy) and an independent content-engagement cr
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