mlsanityData to 7 Oct 2026

Exploring the Meta-level Reasoning of Large Language Models via a Tool-based Multi-hop Tabular Question Answering Task

arXiv 2601.07696 ↗First posted 12 Jan 2026cs.CLcited by 1

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) are increasingly focused on "reasoning" ability, a concept with many overlapping definitions in the LLM discourse. We take a more structured approach, distinguishing meta-level reasoning (denoting the process of reasoning about intermediate steps required to solve a task) from object-level reasoning (which concerns the low-level execution of the aforementioned steps.) We design a novel question answering task, which is based around the values of geopolitical indicators for various countries over various years. Questions require breaking down into intermediate steps, retrieval of data, and mathematical operations over that data. The meta-level reasoning ability of LLMs is analysed by examining the selection of appropriate tools for answering questions. To bring greater depth to the analysis of LLMs beyond final answer accuracy, our task contains 'essential actions' against which we can compare the tool call output of LLMs to infer the strength of reasoning ability. We find that LLMs demonstrate good meta-level reasoning on our task, yet are flawed in some aspects of task understanding. We find that n-shot prompting has little effect on accuracy; error messages encountered do not often deteriorate performance; and provide additional evidence for the poor numeracy of LLMs. Finally, we discuss the generalisation and limitation of our findings to other task domains.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 27 of its 49 references that are in the map. First posted on arXiv 12 Jan 2026.

Builds on

27 of 49 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Augmented Language Models: a Survey15 Feb
  2. 2022Towards Reasoning in Large Language Models: A Survey20 Dec
  3. 2022PAL: Program-aided Language Models18 Nov

Other references · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  8. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  11. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  12. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
12 more
  1. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  2. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  3. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  4. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  5. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  6. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 273
  7. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  8. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 127
  9. 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
  10. 2024What Are Tools Anyway? A Survey from the Language Model Perspectivecited by 53
  11. 2022Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Surveycited by 38
  12. 2023ALIST: Associative Logic for Inference, Storage and Transfer. A Lingua Franca for Inference on the Webcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026LLM Reasoning Is Latent, Not the Chain of Thoughtcited by 0

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  1. 2025Evaluating the Meta- and Object-Level Reasoning of Large Language Models for Question Answering
  2. 2024MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
  3. 2025The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI
  4. 2025Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
  5. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  6. 2024Refining Answer Distributions for Improved Large Language Model Reasoning
  7. 2025Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
  8. 2025Reasoning Capabilities and Invariability of Large Language Models