Source-linked AI summary
Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
Dong-Hai Zhu, Yu-Jie Xiong, Jia-Chen Zhang, Xi-Jiong Xie, Chun-Ming Xia
TL;DR
CoT can overlook critical information in complex reasoning problems. ISP2 prepares context before CoT by extracting and rating information pairs, then iteratively summarizing them; the paper reports performance increases of 7.1% and 8.1%.
Problem
CoT can overlook critical information in highly complex reasoning scenarios, affecting answer quality.
Source: §I · p. 1Method
ISP2 enriches context before CoT by extracting candidate information, rating information pairs, and iteratively summarizing them.
Source: §I · pp. 1–2, C. Iterative Summarization for… · p. 5Results
The authors report performance increases of 7.1% and 8.1% after inserting ISP2.
Source: §I · p. 2Takeaways & Limitations
ISP2 is described as seamlessly integrating with various CoT methods and achieving top performance among SOTA plug-and-play methods.
Source: §I · p. 2
Abstract
from arXiv · showhide
Chain-of-Thought (CoT) Prompting is a dominant paradigm in Large Language Models (LLMs) to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than generating the final answer directly. However, CoT encounters difficulties when key information required for reasoning is implicit or missing. This occurs because CoT emphasizes the sequence of reasoning steps while overlooking the early extraction of essential information. We propose a pre-prompting method called Iterative Summarization Pre-Prompting (ISP^2) to refine LLM reasoning when key information is not explicitly provided. First, entities and their corresponding descriptions are extracted to form potential key information pairs. Next, we use a reliability rating to assess these pairs, then merge the two lowest-ranked pairs into a new entity description. This process is repeated until a unique key information pair is obtained. Finally, that pair, along with the original question, is fed into LLMs to produce the answer. Extensive experiments demonstrate a 7.1% improvement compared to existing methods. Unlike traditional prompting, ISP^2 adopts an inductive approach with pre-prompting, offering flexible integration into diverse reasoning frameworks. The code is available at https://github.com/zdhgreat/ISP-2.
I. INTRODUCTION
The introduction presents ISP2 as a pre-prompting approach that prepares information before CoT reasoning, addressing cases where complex problems contain critical information that CoT may overlook. It reports performance gains and positions ISP2 as compatible with diverse models and CoT methods.
I. INTRODUCTION
- ISP2 is presented as a pre-prompting method that enriches input context before CoT through candidate extraction, reliability rating, and iterative summarization.The authors describe its framing as aligned with Simon’s problem-solving theory.Source: §I · p. 1, Figure 1 · p. 2
- 7.1% and 8.1% performance increases are reported after inserting ISP2; ISP2 with CoT reaches an average performance score of 79.43.The introduction says the average score surpasses other SOTA plug-and-play methods.Source: §I · p. 2
- ISP2 is described as seamlessly integrating with various CoT methods and achieving top performance among SOTA plug-and-play methods.Source: §I · p. 2
- The authors characterize the contribution as extending information extraction into iterative generation and reliability rating for step-by-step information integration.Source: §I · p. 2
- The introduction reports compatibility across closed- and open-source LLM environments, including GPT-3.5 Turbo, GPT-4o-mini, LLaMA2/3, and DeepSeek-Distill.Source: §I · p. 2
II. RELATED WORK
The related work reviews prompting approaches that guide reasoning, contextualize predictions, decompose tasks, or evaluate generated answers. It distinguishes ISP2’s focus on reflecting about problem-space understanding rather than only assessing candidate answers.
A. Chain-of-Thoughts Prompting
- CoT prompting elicits multi-step reasoning, but the related work says complex scenarios still require further refinement.Prior approaches include step-by-step prompting and methods that re-read the question.Source: A. Chain-of-Thoughts Prompting · p. 2
B. In-Context Learning
- In-context learning uses examples embedded in the input context to support task generalization without updating model parameters.Source: B. In-Context Learning · p. 2
C. Task Decomposition
- Task-decomposition methods split complex problems into subproblems, with some approaches linking their order or incrementally combining solutions.Source: C. Task Decomposition · p. 2
D. Self Evaluation
- Self-evaluation methods assess generated answers or actions, whereas this approach applies reflection to problem-space understanding.The related work characterizes that focus as deeper consideration of the problem’s essence.Source: D. Self Evaluation · pp. 2–3
III. PROPOSED APPROACH
The proposed approach formalizes problem context through adaptive extraction, reliability rating, and iterative summarization. Adaptive extraction builds explicit information pairs that serve as anchors for later inference about implicit information.
III. PROPOSED APPROACH
- ISP2 is a plug-and-play pre-prompting method that successively extracts candidate information, rates information pairs, and iteratively summarizes them.The approach describes successive approximation as a way to optimize knowledge representations.Source: §III · p. 3
A. Adaptive Extraction of Candidate Information
- Adaptive extraction uses example-driven prompts to identify question-relevant entities and creates structured descriptions for each retained entity.Descriptions can capture definitional attributes, contextual constraints, and operational relationships.Source: A. Adaptive Extraction of Candidate… · p. 3
- Entity-description pairs summarize explicit problem information as anchors for inferring implicit information, with their number left flexible across questions.Source: A. Adaptive Extraction of Candidate… · pp. 3–4
B. Reliability Rating of Information Pairs
Reliability rating evaluates each information pair’s quality and problem-solving potential in context. It uses LLM judgments, with prompt form and evaluation criteria adapted to task type.
B. Reliability Rating of Information Pairs
- Reliability rating assesses an information pair’s quality, completeness, and problem-solving potential conditioned on the preceding pairs.The score is an expectation over the LLM’s probability distribution.Source: B. Reliability Rating of Information… · p. 4
- The evaluation integrates fluency, relevance, and completeness dynamically rather than using a fixed weighted sum.Source: B. Reliability Rating of Information… · p. 4
- When exact expectation computation is infeasible or costly, the method estimates reliability by averaging k independent samples; experiments set k to 3.The number of samples controls the estimation-accuracy and computational-cost trade-off.Source: B. Reliability Rating of Information… · p. 4
- Reliability prompts can request scalar scores or opinion-based judgments, with task-specific criteria emphasizing derivation correctness for math and coherence and relevance for commonsense tasks.Source: B. Reliability Rating of Information… · p. 4
C. Iterative Summarization for Knowledge Understanding
ISP2 iteratively refines information pairs by merging the two lowest-reliability pairs and reassessing the result. It continues until a single information pair remains.
C. Iterative Summarization for Knowledge Understanding
- ISP2 repeatedly selects two low-reliability information pairs, synthesizes them into composite knowledge, and rates the new pair.The process repeats these three operations to refine fragmented information.Source: C. Iterative Summarization for… · pp. 4–5
- Each iteration merges the two pairs with the lowest reliability scores rather than choosing higher-scoring pairs.The authors describe the low-scoring pairs as potentially complementary fragments of knowledge.Source: C. Iterative Summarization for… · pp. 4–5
- In the Bolts/Robe example, merging the pairs produces a “Total bolts” description with a reliability score of 0.78.The new description combines the fiber quantities needed for a robe.Source: C. Iterative Summarization for… · p. 5
- After a merge, the new pair replaces the two originals and the reliability scores of the list are recalculated.The merging operation is described as following a probabilistic maximization principle; p_k is the contextual probability of the k-th knowledge element given context x.Source: C. Iterative Summarization for… · p. 5
- Iteration ends when the list converges to one information pair, regardless of that pair’s reliability score.The stated criterion ensures termination when no further merging is possible.Source: C. Iterative Summarization for… · p. 5
D. Question Answering
For question answering, ISP2 adds a refined information pair to the prompt context before the model reasons. The section illustrates this with a travel-distance problem and describes compatibility with different CoT prompting methods.
D. Question Answering
- In the example, ISP2 extracts speed and travel time, then CoT multiplies 20 km/hr by 2.5 hours to answer 50 km.The answer is option E.Source: D. Question Answering · p. 5
- The model is framed as generating final result y through intermediate reasoning z from input x, which ISP2 augments with the information pair IP.The input context is the prompt T and question Q; the passage describes y and z as conditionally independent given x.Source: D. Question Answering · pp. 5–6
- ISP2 refines information pairs before inference and combines the resulting unique pair with the question for the final reasoning process.The method is presented as integrating information extraction with cognitive summarization.Source: D. Question Answering · pp. 5–6
- ISP2 is designed to work with different CoT prompting methods, from simple step-by-step reasoning to more sophisticated in-context learning.The section says it dynamically integrates the appropriate prompting strategy for the task.Source: D. Question Answering · p. 6
IV. EXPERIMENTS
Experiments evaluate ISP2 across six datasets spanning mathematical and commonsense reasoning, with several prompting methods and LLMs. The reported results show improvements across models and strong comparisons with plug-and-play methods.
A. Experimental Setup
- The evaluation covers six datasets and compares ISP2 integrations with CoT, Complex CoT, PoT, and Self Consistency, using few-shot prompts without training or fine-tuning.Most datasets use four exemplars, and testing includes GPT-3.5, GPT-4o-mini, LLaMA2, LLaMA3, and DeepSeek-Distill-Qwen-7B.Source: A. Experimental Setup · p. 6
B. Main Results
- Average improvements were 1.1% for GPT-4o-mini, 7.1% for GPT-3.5, 4.5% for DeepSeek-Qwen-7B, 5.2% for LLaMA3-8B, 8.1% for LLaMA2-13B, and 12.4% for LLaMA2-7B.The authors report these averages as evidence of improved performance after ISP2 processing.Source: B. Main Results · p. 7
- On math word problems, ISP2 improved standard reasoning and code-generation approaches, with particularly significant enhancement alongside Self Consistency.The authors attribute gains to filtering misleading information and revealing key data; they also report that algebraic information pairs can be stored in formulaic forms for AQuA.Source: B. Main Results · p. 7, Table III · p. 7
- ISP2 reached new best levels on AddSub and SVAMP and second-best levels on GSM8K and AQuA among current SOTA plug-and-play methods in mathematics.Table II compares existing plug-and-play methods on GPT-3.5.Source: B. Main Results · p. 7, Table II · p. 7
- ISP2 increased performance on StrategyQA by 4.9% and CommonsenseQA by 2.8%, while StrategyQA with LLaMA2 showed no significant improvement.Across most commonsense experiments, ISP2 outperformed most base prompts; it performed comparably to ERA on StrategyQA and ranked second-best on CommonsenseQA.Source: B. Main Results · p. 8, Figure 4 · p. 8
C. Ablation Studies
The ablations compare entity-only extraction, information-pair extraction, and alternative pair-selection strategies. Complete information pairs and iterative summarization of low-scoring pairs are associated with stronger reasoning performance.
C. Ablation Studies
- OE supplies named entities alone, OIP supplies extracted information pairs without summarization, and SAISP2 instead summarizes the two highest-scoring pairs.The full ISP2 selection strategy summarizes the two lowest-scoring pairs.Source: C. Ablation Studies · p. 8
- Across six datasets, adding entities and complete information pairs improved CoT and Complex CoT, with OIP benefiting problem reasoning more than OE.OIP supplies structured contextual descriptions, while OE provides scattered entities that the model must reconstruct during reasoning.Source: C. Ablation Studies · p. 8
- Selecting the two highest-scoring pairs underperformed, while summarizing low-scoring pairs yielded superior results.The authors attribute this contrast to high-scoring pairs' surface relevance saturating early, whereas lower-scoring pairs can contribute indirect associations and conflicting evidence.Source: C. Ablation Studies · pp. 8–9
- In mathematical tasks, high-scoring pairs may repeat one solution path and miss edge cases, limiting further refinement of reasoning.Source: C. Ablation Studies · p. 9
A. Summarization Steps Analysis
Across six datasets, short iterative-summarization steps commonly provide effective information for answering questions. The effect of longer steps differs between LLaMA and GPT-3.5.
A. Summarization Steps Analysis
- Short summarization steps consistently provide effective information for answering questions across six datasets.Step-length distributions predominantly fall in the short-step category; Figure 5 presents step lengths and accuracy across LLMs and datasets.Source: A. Summarization Steps Analysis · p. 9, Figure 5 · p. 9
- Adaptive extraction filters less helpful information, compressing the steps and simplifying the reasoning process.Source: A. Summarization Steps Analysis · p. 9
- Longer steps can assist LLaMA, but may provide little support to GPT-3.5.The authors suggest extensive summaries can reinforce knowledge for LLaMA, whereas excessive length may interfere with GPT-3.5's integration coherence.Source: A. Summarization Steps Analysis · p. 9
B. Error Source Analysis
The error analysis identifies failures at extraction, summarization, and reasoning stages, with extraction errors most common overall and mathematical extraction errors rising with task complexity. The conclusion presents ISP2 as an iterative information-understanding method that can be combined with CoT and Self-Consistency.
B. Error Source Analysis
- The analysis classifies ISP2 failures as information-pair extraction, summarization, or reasoning errors, which can propagate to later steps.Source: B. Error Source Analysis · p. 9
- Information-pair extraction errors are most frequent overall, while summarization and inference errors are lower; in commonsense datasets, extraction and summarization rates are close.The table displays the distribution of error categories across datasets.Source: B. Error Source Analysis · p. 9, Table VIII · p. 10
- Mathematical extraction errors increase with dataset complexity, and computation errors can persist during both summarization and inference.The authors also report that summarization improves dataset accuracy, while mathematical computation deficiencies remain.Source: B. Error Source Analysis · p. 10
VI. CONCLUSION
- ISP2 uses precise information extraction and iterative summarization, and the authors report that it can combine with CoT and Self-Consistency to improve reasoning.Source: §VI · p. 10