mlsanityData to 14 Oct 2026

Agent Instructs Large Language Models to be General Zero-Shot Reasoners

arXiv 2310.03710 ↗First posted 5 Oct 2023cs.CL cs.AI cs.LGcited by 32

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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Abstract (arXiv)

We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to instruct the reasoning process of large language models. We show this approach further unleashes the zero-shot reasoning abilities of large language models to more tasks. We study the performance of our method on a wide set of datasets spanning generation, classification, and reasoning. We show that our method generalizes to most tasks and obtains state-of-the-art zero-shot performance on 20 of the 29 datasets that we evaluate. For instance, our method boosts the performance of state-of-the-art large language models by a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement in reasoning is striking, with an average increase of 10.5%. With our method, Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 0 of its 106 references that are in the map. First posted on arXiv 5 Oct 2023.

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Citing papers per year: 2023: 2; 2024: 18; 2025: 10; 2026: 2.

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  9. 2024Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizerscited by 35
  10. 2023A Zero-Shot Language Agent for Computer Control with Structured Reflectioncited by 28
  11. 2025Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systemscited by 23
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  3. 2024Can We Verify Step by Step for Incorrect Answer Detection?cited by 15
  4. 2025LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chesscited by 15
  5. 2024Measuring Vision-Language STEM Skills of Neural Modelscited by 14
  6. 2025An Empirical Study of Agent Developer Practices in AI Agent Frameworkscited by 14
  7. 2024DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMscited by 5
  8. 2024Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Studycited by 5
  9. 2025Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectivescited by 5
  10. 2026MOC: Multi-Order Communication in LLM-based Multi-Agent Systemscited by 4
  11. 2024Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answeringcited by 3
  12. 2025Weak-to-Strong Generalization with Failure Trajectories: A Tree-based Approach to Elicit Optimal Policy in Strong Modelscited by 3
  13. 2023The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems: A Scoping Surveycited by 2
  14. 2024Towards Dialogues for Joint Human-AI Reasoning and Value Alignmentcited by 2
  15. 2024Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuningcited by 2
  16. 2025Predicting Task Performance with Context-aware Scaling Lawscited by 2
  17. 2025LLM Enhancer: Merged Approach using Vector Embedding for Reducing Large Language Model Hallucinations with External Knowledgecited by 1
  18. 2025From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviewscited by 1
  19. 2025Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysiscited by 0
  20. 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Productscited by 0

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  7. 2023IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models
  8. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches