Why Large Language Models Miss Instructions and How to Fix It

The Challenge of Skipped Instructions in Large Language Models

Large Language Models (LLMs) have become essential in powering AI applications like chatbots, content creation, and coding help. However, a frequent problem users encounter is that LLMs sometimes skip parts of instructions, especially when the instructions are lengthy or involve multiple steps. This leads to incomplete or inaccurate outputs, reducing trust and usability.

Reasons Behind Instruction Skipping

LLMs process input as tokens sequentially, giving more attention to earlier parts of the input. As input length increases or instructions become complex, the model's limited attention capacity causes it to lose focus on later instructions. Overlapping or conflicting instructions increase complexity, causing confusion and vague responses. The models are also biased towards simple instructions due to their training data and have token limits that truncate input, causing later instructions to be ignored.

Insights from the SIFo 2024 Benchmark

The Sequential Instructions Following (SIFo) Benchmark 2024 tested LLMs on tasks requiring step-by-step completion. Even top models like GPT-4 and Claude-3 struggle to fully complete long or complex instruction sequences. Key challenges include understanding each instruction, reasoning across them logically, and producing reliable, complete outputs. Techniques like prompt engineering and RLHF improve performance but do not eliminate skipping.

Technical and Practical Causes

The attention mechanism dilutes focus on later tokens in long prompts, and token limits truncate instructions. Ambiguous or conflicting instructions create output ambiguity, while poor prompt formatting hinders instruction separation. Prompt engineering improves adherence by clarifying structure.

Best Practices to Reduce Instruction Skipping

• Break down long or multi-step tasks into smaller focused prompts.
• Use numbered lists or bullet points to clearly separate instructions.
• Provide explicit, unambiguous instructions emphasizing that no steps should be skipped.
• For critical tasks, submit each instruction as a separate prompt.

Advanced Strategies for Efficiency and Completeness

• Batch related instructions with clear formatting and explicit labels.
• Use chain-of-thought prompting to guide sequential reasoning.
• Add explicit reminders like “Answer every task completely” and “Do not skip any instruction.”
• Test different LLMs and parameter settings to find the best fit.

Model Fine-Tuning and External Tools

Fine-tuning models on multi-step instruction datasets and using Reinforcement Learning with Human Feedback can enhance following instructions. Integration with external APIs or plugins can provide extra context and improve output accuracy.

Summary

LLMs’ tendency to skip instructions is due to their sequential token processing, limited attention, and training biases. Clear, simple, and well-structured prompts improve results. Breaking tasks into smaller parts, using formatting, and employing advanced prompting techniques help balance accuracy and efficiency. Testing models and fine-tuning further boost performance, enabling more reliable AI assistance.