mlsanityData to 7 Oct 2026

Chain-Of-Thought Prompting Under Streaming Batch: A Case Study

arXiv 2306.00550 ↗First posted 1 Jun 2023cs.LG cs.AI cs.CLcited by 1

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities. Chain-of-Thought (CoT) has been proposed as a way of assisting LLMs in performing complex reasoning. However, developing effective prompts can be a challenging and labor-intensive task. Many studies come out of some way to automatically construct CoT from test data. Most of them assume that all test data is visible before testing and only select a small subset to generate rationales, which is an unrealistic assumption. In this paper, we present a case study on how to construct and optimize chain-of-thought prompting using batch data in streaming settings.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 17 of its 17 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

17 of 17 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Reasoning Implicit Sentiment with Chain-of-Thought Prompting18 May
  2. 2023Active Prompting with Chain-of-Thought for Large Language Models23 Feb
  3. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters20 Dec
  4. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  5. 2022Large Language Models are Zero-Shot Reasoners24 May
  6. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  8. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 9

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2022Emergent Abilities of Large Language Modelscited by 2,483
  3. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  4. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
  5. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  6. 2022Language Models are Multilingual Chain-of-Thought Reasonerscited by 518
  7. 2023MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Actioncited by 511
  8. 2016Solving General Arithmetic Word Problemscited by 353
  9. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?cited by 151

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Modelscited by 11

Similar, not cited

Close in wording, no citation either way

  1. 2023Towards Better Chain-of-Thought Prompting Strategies: A Survey
  2. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
  3. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  4. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
  5. 2024ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
  6. 2023Stress Testing Chain-of-Thought Prompting for Large Language Models
  7. 2025Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
  8. 2025Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting