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Chain-of-thought Reviewing and Correction for Time Series Question Answering

Chen Su, Yuanhe Tian, Yan Song

TL;DR

The paper addresses the challenge of reliable TSQA with LLMs, where pure text-based reasoning can falter on numerical sequences. It introduces T3LLM, a three-LLM workflow comprising a worker, reviewer, and student that generates, reviews, and internalizes corrected chain-of-thought to tackle TSQA tasks. A correction loop leverages the verifiability of time-series data to prune erroneous reasoning and guide subsequent steps, with corrected CoTs used to fine-tune a compact student model. Experiments on CTQA and TMQA show state-of-the-art performance across MCQ, T/F, and open-ended tasks, demonstrating improved robustness and interpretability through self-corrected CoT training.

Abstract

With the advancement of large language models (LLMs), diverse time series analysis tasks are reformulated as time series question answering (TSQA) through a unified natural language interface. However, existing LLM-based approaches largely adopt general natural language processing techniques and are prone to reasoning errors when handling complex numerical sequences. Different from purely textual tasks, time series data are inherently verifiable, enabling consistency checking between reasoning steps and the original input. Motivated by this property, we propose T3LLM, which performs multi-step reasoning with an explicit correction mechanism for time series question answering. The T3LLM framework consists of three LLMs, namely, a worker, a reviewer, and a student, that are responsible for generation, review, and reasoning learning, respectively. Within this framework, the worker generates step-wise chains of thought (CoT) under structured prompts, while the reviewer inspects the reasoning, identifies erroneous steps, and provides corrective comments. The collaboratively generated corrected CoT are used to fine-tune the student model, internalizing multi-step reasoning and self-correction into its parameters. Experiments on multiple real-world TSQA benchmarks demonstrate that T3LLM achieves state-of-the-art performance over strong LLM-based baselines.

Chain-of-thought Reviewing and Correction for Time Series Question Answering

TL;DR

The paper addresses the challenge of reliable TSQA with LLMs, where pure text-based reasoning can falter on numerical sequences. It introduces T3LLM, a three-LLM workflow comprising a worker, reviewer, and student that generates, reviews, and internalizes corrected chain-of-thought to tackle TSQA tasks. A correction loop leverages the verifiability of time-series data to prune erroneous reasoning and guide subsequent steps, with corrected CoTs used to fine-tune a compact student model. Experiments on CTQA and TMQA show state-of-the-art performance across MCQ, T/F, and open-ended tasks, demonstrating improved robustness and interpretability through self-corrected CoT training.

Abstract

With the advancement of large language models (LLMs), diverse time series analysis tasks are reformulated as time series question answering (TSQA) through a unified natural language interface. However, existing LLM-based approaches largely adopt general natural language processing techniques and are prone to reasoning errors when handling complex numerical sequences. Different from purely textual tasks, time series data are inherently verifiable, enabling consistency checking between reasoning steps and the original input. Motivated by this property, we propose T3LLM, which performs multi-step reasoning with an explicit correction mechanism for time series question answering. The T3LLM framework consists of three LLMs, namely, a worker, a reviewer, and a student, that are responsible for generation, review, and reasoning learning, respectively. Within this framework, the worker generates step-wise chains of thought (CoT) under structured prompts, while the reviewer inspects the reasoning, identifies erroneous steps, and provides corrective comments. The collaboratively generated corrected CoT are used to fine-tune the student model, internalizing multi-step reasoning and self-correction into its parameters. Experiments on multiple real-world TSQA benchmarks demonstrate that T3LLM achieves state-of-the-art performance over strong LLM-based baselines.
Paper Structure (18 sections, 3 equations, 6 figures, 4 tables)

This paper contains 18 sections, 3 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. The Approach
  3. Primitive CoT Generation
  4. CoT Review and Correction
  5. Student Fine-tuning
  6. Experiment Settings
  7. Datasets
  8. Baselines and Comparing Approaches
  9. Implementation Details
  10. Results and Analysis
  11. Overall Results
  12. Effect of Different $\mathcal{MCR}$
  13. Effect of Different Reviewer LLMs
  14. Effect of Different Worker LLMs
  15. Case Study
  16. ...and 3 more sections

Figures (6)

  • Figure 1: The illustration of our approach. The left side defines the TSQA task and the working, reviewing, and continuing prompt templates. The middle depicts the overall process of T3LLM. From top to bottom, they represent the primitive CoT generation, the correction loop, and the fine-tuning based on the CoTs. The right side shows the three types of TSQA tasks (i.e., multi-choice questions, true-or-false judgments, and open-ended questions).
  • Figure 2: The performance of our approach with different numbers of maximum correction rounds $\mathcal{MCR}=\{1,2,3,4,5,10\}$ on the TMQA dataset.
  • Figure 3: Case study with four examples on MCQ and T/F questions, and time series forecasting and classification tasks in OPE questions from the TMQA dataset. The left side presents the question and the corresponding time series. The right side show Time-MQA, TSCoT and T3LLM's reasoning process and their corresponding answer.
  • Figure 4: The working prompt template used in T3LLM.
  • Figure 5: The reviewing prompt template used in T3LLM.
  • ...and 1 more figures