DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction

• URL: http://arxiv.org/abs/2510.09211v2
• Date: Mon, 20 Oct 2025 08:57:20 GMT
• Title: DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction
• Authors: Yiqi Li, Yusheng Liao, Zhe Chen, Yanfeng Wang, Yu Wang,
• Abstract summary: Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions.<n>Fine-tuning LLMs on supervised datasets to address this is impractical due to high computational costs and limited parameter access.<n>We propose DICE, a lightweight framework that guides small language models (SLMs) to refine LLMs' outputs through chain-of-thought (CoT) correction.
• Score: 29.22321321753093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When performing reasoning tasks with user-specific requirements, such as strict output formats, large language models (LLMs) often prioritize reasoning over adherence to detailed instructions. Fine-tuning LLMs on supervised datasets to address this is impractical due to high computational costs and limited parameter access. To tackle this, we propose DICE, a lightweight framework that guides small language models (SLMs) to refine LLMs' outputs through chain-of-thought (CoT) correction. DICE decouples the process by first prompting LLMs to generate natural language responses, then using trained SLMs to analyze and refine these outputs to meet structured output specifications. This framework preserves LLMs' broad knowledge and reasoning capabilities while ensuring the outputs conform to user demands. Specifically, DICE first constructs structured CoT adaptation datasets via a two-stage method and subsequently applies a dual-tuning strategy to fine-tune SLMs for generating structured outputs in an analyze-then-answer pattern. Experiments demonstrate that DICE improves the average format accuracy and content correctness of LLM outputs by 35.4\% and 29.4\%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines.

Related papers

• Serialized Output Prompting for Large Language Model-based Multi-Talker Speech Recognition [27.16462631523899]
  Existing multi-talker (MT) automatic speech recognition (ASR) systems either omit prompts or rely on simple task-definition prompts.<n>We propose extracting serialized output prompts ( SOP) and explicitly guiding the LLM using structured prompts to improve system performance.<n>The proposed SOP approach significantly improved performance under both two- and three-talker conditions.
  arXiv  Detail & Related papers  (2025-09-01T03:10:14Z)
• An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning [52.29223403698673]
  This paper examines the use of Conformal Language Modelling (CLM) alongside Answer Set Programming (ASP)<n>We apply CLM to generate sets of ASP programs from an LLM, providing statistical guarantees on the correctness of the outputs.<n> Experimental results show that CLM significantly outperforms baseline models that use standard sampling methods.
  arXiv  Detail & Related papers  (2025-03-07T14:10:10Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
  Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets.<n>This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
  arXiv  Detail & Related papers  (2025-01-08T11:37:06Z)
• LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding [19.510385758079966]
  Large Language Model Aided Real-time Scene Recommendation(LARR) This paper introduces Large Language Model Aided Real-time Scene Recommendation(LARR)
  arXiv  Detail & Related papers  (2024-08-21T10:56:26Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation [15.153844486572932]
  This paper proposes a practice-friendly LLM-enhanced paradigm with preference parsing (P2Rec) for sequential recommender systems (SRS) Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model. Our goal is to let LLM learn to reconstruct a corresponding prior preference distribution from each user's interaction sequence.
  arXiv  Detail & Related papers  (2024-06-01T07:18:56Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.