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Active Prompting with Chain-of-Thought for Large Language Models
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The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answer tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving state-of-the-art on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationship demonstrate the effectiveness of our method. Our code will be available at https://github.com/shizhediao/active-prompt.
Forward citations
Cited by 7 Pith papers
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The Challenge of Teaching Reasoning to LLMs Without RL or Distillation
Twenty high-quality chain-of-thought examples from a reasoning model are enough to activate strong math reasoning in a 32B base model with lightweight fine-tuning.
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Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting
On-device LLMs use measurably different amounts of energy depending on the prompt verb, but the effect is mostly a side-effect of response length and is inconsistent across models.
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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...
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Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve
On GSM8K, zero-shot free-form generation beats few-shot CoT prompting for Mathstral, Qwen2.5, and Llama-3.1, suggesting standard CoT baselines can underestimate modern reasoning models.
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Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic
Stepwise CoT confidence is reshaped and scored with signal temporal logic robustness to produce better calibrated confidence estimates on Gaokao math questions.
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Graph-of-Causal Evolution: Challenging Chain-of-Model for Reasoning
GoCE swaps CoM's chain structure for a differentiable causal graph and reports accuracy gains on CLUTRR, CLadder, EX-FEVER, and CausalQA, but the evidence is sandbox-generated and unauditable.
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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
SCOUT combines progressive distillation with a cross-attention module to make recursive latent reasoning work through fine-tuning, yielding up to 1.8% accuracy gains over standard fine-tuning.
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