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Emergent Response Planning in LLMs
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Shows that LLM prompt representations linearly/MLP-probe-decodably encode global attributes of their future responses (length, reasoning steps, character choice, answer, confidence, factual stance), termed emergent response planning.
Problem
Whether LLMs, trained only for next-token prediction, plan beyond the next token by encoding global attributes of their entire upcoming response in prompt representations.
Approach
Define response planning as probe-decodable global response attributes from prompt-token hidden states; train one-hidden-layer MLP probes on prompt representations to predict structure, content, and behavior attributes of greedy-decoded responses, with prompt designs that block first-token shortcuts.
Key results
- Fine-tuned models show strong linear correlations between probed and true response length and reasoning steps, while classification attributes (character choice, MC answer, confidence, factual consistency) are above random baselines by F1.
- Probing outperforms verbalized self-estimation: base models are near or worse than random at self-estimating response length and character choice, while fine-tuned models improve only marginally.
- Within each model family (Llama-2-chat, Llama-3-Instruct, Qwen-2-Instruct, Qwen-2.5-Instruct; 1.5B-72B), larger models show stronger planning for structure and content attributes, but the scaling trend does not transfer across families.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use fine-tuned rather than base models | LLM response-attribute probing · In-dataset structure prediction | correlation with ground truth | Helped |
| Use fine-tuned rather than base models | LLM response-attribute probing · Cross-dataset structure prediction | correlation with target labels | Helped |
| Increase model size within a model family (ablation) | Llama-2-chat, Llama-3-Instruct, Qwen-2-Instruct, and Qwen-2.5-Instruct · UltraChat and TinyStories | planning capability | Helped |
| Increase model size across different model families (ablation) | LLM response-attribute probing · UltraChat and TinyStories | planning capability | Mixed |
| Vary MLP probe hidden size (ablation) | MLP probes for all six response attributes · All probing tasks | probe performance | Mixed |
| Increase MLP hidden size for structure attributes (ablation) | MLP probes · Response length and reasoning steps | probe performance | Helped |
| Increase MLP hidden size for content attributes (ablation) | MLP probes · Character choices and multiple-choice answers | probe performance | Helped |
| Increase MLP hidden size for behavior attributes (ablation) | MLP probes · Answer confidence and factual consistency | probe performance | Helped |
| Probe intermediate rather than early or final model layers (ablation) | LLM response-attribute probing · Structure attributes | prediction performance | Helped |
| Probe later rather than early model layers (ablation) | LLM response-attribute probing · Content attributes | prediction performance | Helped |
| Probe after the initial model layers (ablation) | LLM response-attribute probing · Behavior attributes | prediction performance | Helped |
| Probe at middle rather than initial or late response positions (ablation) | Character-choice and answer-confidence prediction · TinyStories and MedMCQA | probing accuracy | Hurt |
Surprising findings
- Probes exceed models' own verbalized self-estimates; base models are near or below random at self-estimating attributes.
- Planning signal for response length and character choice is high at the start of generation, dips mid-response, and recovers toward the end.
Limitations
- Uses greedy decoding for labels, so sampling behavior is not captured.
- Possible first-token shortcut confounds; mitigated by prompt design but not fully ruled out.
- Causal role of planning representations is not established.
Experimental setup
Datasets: Ultrachat AlpacaEval GSM8K MATH TinyStories ROCStories CommonsenseQA SocialIQA MedMCQA ARC-Challenge CREAK FEVER
Benchmarks: Ultrachat AlpacaEval GSM8K MATH TinyStories ROCStories CommonsenseQA SocialIQA MedMCQA ARC-Challenge CREAK FEVER
Metrics: Spearman Kendall Pearson F1 accuracy
Baselines: Self-Estimate (verbalized self-prediction)
Builds on: Future Lens
Closest papers by meaning
- Latent Planning Emerges with Scale (ICLR 2026 · similarity 0.90)
- Do LLMs Build World Representations? Probing Through the Lens of State Abstraction (NeurIPS 2024 · similarity 0.89)
- Exploring the LLM Journey from Cognition to Expression with Linear Representations (ICML 2024 · similarity 0.88)
- Understanding Emergent Abilities of Language Models from the Loss Perspective (NeurIPS 2024 · similarity 0.88)
- Do LLMs ``know'' internally when they follow instructions? (ICLR 2025 · similarity 0.88)
- CogBench: a large language model walks into a psychology lab (ICML 2024 · similarity 0.87)
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