pith. sign in

arxiv: 2506.16616 · v3 · submitted 2025-06-19 · 💻 cs.DB

LDI: Localized Data Imputation for Text-Rich Tables

Soroush Omidvartehrani , Davood Rafiei This is my paper

Pith reviewed 2026-05-19 08:47 UTC · model grok-4.3

classification 💻 cs.DB
keywords data imputationmissing valuestext-rich tableslarge language modelslocalized reasoningtabular datainterpretability
0
0 comments X

The pith

LDI imputes missing values in text-rich tables by directing LLMs to reason over small relevant subsets instead of full tables.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Tables packed with text often have missing entries whose connections are scattered and hard to spot. LDI addresses this by using large language models to examine only a compact group of related attributes and rows for each missing spot. The method reduces irrelevant information, speeds up processing, and shows users precisely which pieces of data drove the fill-in and the reasons they were selected. Experiments across real-world and synthetic tables indicate LDI beats prior imputation techniques, with accuracy lifts of as much as 8 percent for large hosted models and larger lifts for smaller local models. The added clarity in explanations also supports use in settings where decisions depend on trustworthy data repairs.

Core claim

LDI is a novel framework that leverages LLMs through localized reasoning, selecting a compact, contextually relevant subset of attributes and tuples for each missing value. This targeted selection reduces noise, improves scalability, and provides transparent attribution by revealing the dependency relations that justify each selected attribute and the evidence behind each retrieved tuple.

What carries the argument

Localized reasoning, which selects a compact, contextually relevant subset of attributes and tuples for each missing value to guide LLM imputation.

If this is right

  • Higher imputation accuracy than existing methods across both real and synthetic datasets.
  • Larger accuracy gains when the underlying LLM is a small local model rather than a hosted one.
  • Explicit attribution that shows both the chosen data and the dependency links that led to its selection.
  • Greater robustness and interpretability that suits high-stakes data management tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same localized selection could be tested on other table operations such as error detection or consistency checking.
  • Limiting inputs to small subsets may cut token usage and cost when applying LLMs to very wide or long tables.
  • Automatically learning how large the relevant subset should be for different table types could strengthen results further.

Load-bearing premise

LLMs can reliably identify and reason over a compact, contextually relevant subset of attributes and tuples for each missing value without missing critical dependencies or introducing selection bias.

What would settle it

Construct a text-rich table where the information needed to impute a missing value is deliberately spread across many rows and columns that the localized selector would skip, then measure whether LDI accuracy falls below non-localized baselines.

Figures

Figures reproduced from arXiv: 2506.16616 by Davood Rafiei, Soroush Omidvartehrani.

Figure 1
Figure 1. Figure 1: Example of data imputation subset of the table that contains the most relevant information for the missing entry. Our approach is to identify this localized context prior to imputation by decomposing the problem into two sub-tasks: (1) selecting a subset of columns that are most relevant to the column with missing values, and (2) identifying a subset of rows that provide sufficient contextual evidence for … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the LDI framework A. Attribute Selection To identify attributes most relevant to the imputation target, LDI performs attribute selection in three phases: (1) selecting a representative subset of tuples, (2) detecting group-specific patterns in candidate columns, and (3) evaluating approximate dependency relationships. This section introduces the relaxed dependency criterion we use and outlines … view at source ↗
Figure 3
Figure 3. Figure 3: Example of data imputation (after applying our approach) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Missing values are pervasive in real-world tabular data and can significantly impair downstream analysis. Imputing them is especially challenging in text-rich tables, where dependencies are implicit, complex, and dispersed across long textual fields. Recent work has explored using Large Language Models (LLMs) for data imputation, yet existing approaches typically process entire tables or loosely related contexts, which can compromise accuracy, scalability, and explainability. We introduce LDI, a novel framework that leverages LLMs through localized reasoning, selecting a compact, contextually relevant subset of attributes and tuples for each missing value. This targeted selection reduces noise, improves scalability, and provides transparent attribution by revealing the dependency relations that justify each selected attribute and the evidence behind each retrieved tuple. It makes clear not only which data influenced a prediction, but also why it was chosen. Through extensive experiments on real and synthetic datasets, we demonstrate that LDI consistently outperforms state-of-the-art imputation methods, achieving up to 8% higher accuracy with hosted LLMs and even greater gains with small local models. The improved interpretability and robustness also make LDI well-suited for high-stakes data management applications. Our code and datasets are publicly available at https://github.com/soroushomidvar/LDI.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces LDI, a framework for imputing missing values in text-rich tables using LLMs via localized reasoning. For each missing value, the method selects a compact, contextually relevant subset of attributes and tuples, with the goal of reducing noise, improving scalability, and providing explainability by revealing dependency relations and evidence for selections. Experiments on real and synthetic datasets are reported to show consistent outperformance over state-of-the-art imputation methods, with up to 8% higher accuracy using hosted LLMs and larger gains with small local models. Code and datasets are released publicly.

Significance. If the accuracy improvements can be attributed specifically to the localized selection and reasoning mechanism, LDI could meaningfully advance LLM-assisted data imputation for complex, text-heavy tabular data in database systems. The focus on interpretability and transparent attribution of influences is a notable strength for high-stakes applications. The public availability of code and datasets supports reproducibility and is a positive contribution.

major comments (1)
  1. [Experimental Evaluation] Experimental Evaluation (results section): The headline claim of up to 8% accuracy improvement is supported only by aggregate end-to-end imputation accuracy across datasets. No ablation studies or metrics are provided to verify the quality of the LLM-driven localized selection (e.g., precision/recall of selected attributes and tuples against ground-truth relevant fields on synthetic data, or direct comparison to non-localized LLM baselines with equivalent context size). This makes it impossible to isolate whether gains arise from the localization mechanism itself or from prompt design, model choice, or dataset characteristics.
minor comments (2)
  1. [Abstract] The abstract states 'even greater gains with small local models' without quantifying the improvement or naming the specific models and datasets involved.
  2. [Method] Notation for the selection step (e.g., how the compact subset is formally defined or scored) could be clarified with a small example or pseudocode early in the method description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of LDI for advancing LLM-assisted imputation in text-rich tables. We address the major comment below and will revise the manuscript accordingly to strengthen the experimental evaluation.

read point-by-point responses

  1. Referee: [Experimental Evaluation] Experimental Evaluation (results section): The headline claim of up to 8% accuracy improvement is supported only by aggregate end-to-end imputation accuracy across datasets. No ablation studies or metrics are provided to verify the quality of the LLM-driven localized selection (e.g., precision/recall of selected attributes and tuples against ground-truth relevant fields on synthetic data, or direct comparison to non-localized LLM baselines with equivalent context size). This makes it impossible to isolate whether gains arise from the localization mechanism itself or from prompt design, model choice, or dataset characteristics.

    Authors: We agree that additional analyses are needed to more precisely attribute performance gains to the localized selection mechanism. While the manuscript already reports consistent improvements over prior methods on both real-world and synthetic datasets (with larger relative gains for smaller local models), we acknowledge the absence of explicit ablations isolating localization from prompt design or context size. In the revision, we will add: (1) direct comparisons to non-localized LLM baselines that receive equivalent total context size, and (2) on the synthetic datasets, precision/recall metrics for the selected attributes and tuples against the ground-truth relevant fields. These results will be presented in the experimental section to clarify the contribution of the localization and dependency-revealing steps. revision: yes

Circularity Check

0 steps flagged

Empirical framework with external validation shows no circularity

full rationale

The paper introduces LDI as a novel localized reasoning framework that selects compact attribute/tuple subsets for each missing value before LLM imputation. Its performance claims rest on direct experimental comparisons against state-of-the-art methods across real and synthetic datasets, with reported accuracy gains. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described approach; the method is presented as an independent algorithmic contribution whose value is assessed externally rather than by construction from its own inputs or prior author results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests primarily on the domain assumption that LLMs can perform accurate localized reasoning on small selected subsets; no free parameters or new invented entities are described in the abstract.

axioms (1)
  • domain assumption Large language models can accurately select and reason over compact, contextually relevant subsets of attributes and tuples to impute missing values in text-rich tables.
    This assumption underpins the localized reasoning step and the claimed improvements in accuracy and explainability.

pith-pipeline@v0.9.0 · 5751 in / 1228 out tokens · 42427 ms · 2026-05-19T08:47:20.598110+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages · 2 internal anchors

  1. [1]

    A survey on missing data in machine learning,

    T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, “A survey on missing data in machine learning,” Journal of Big data, vol. 8, pp. 1–37, 2021

    work page 2021

  2. [2]

    Missing value imputation based on data clustering,

    S. Zhang, J. Zhang, X. Zhu, Y . Qin, and C. Zhang, “Missing value imputation based on data clustering,” in Transactions on computational science I. Springer, 2008, pp. 128–138

    work page 2008

  3. [3]

    Data management in machine learning: Challenges, techniques, and systems,

    A. Kumar, M. Boehm, and J. Yang, “Data management in machine learning: Challenges, techniques, and systems,” in Proceedings of the 2017 ACM International Conference on Management of Data , 2017, pp. 1717–1722

    work page 2017

  4. [4]

    The prevention and handling of the missing data,

    H. Kang, “The prevention and handling of the missing data,” Korean journal of anesthesiology , vol. 64, no. 5, pp. 402–406, 2013

    work page 2013

  5. [5]

    Gbkii: An imputation method for missing values,

    C. Zhang, X. Zhu, J. Zhang, Y . Qin, and S. Zhang, “Gbkii: An imputation method for missing values,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 2007, pp. 1080–1087

    work page 2007

  6. [6]

    Semi-parametric optimization for missing data imputation,

    Y . Qin, S. Zhang, X. Zhu, J. Zhang, and C. Zhang, “Semi-parametric optimization for missing data imputation,” Applied Intelligence, vol. 27, no. 1, pp. 79–88, 2007

    work page 2007

  7. [7]

    Responsible data man- agement,

    J. Stoyanovich, B. Howe, and H. V . Jagadish, “Responsible data man- agement,” Proceedings of the VLDB Endowment , vol. 13, no. 12, 2020

    work page 2020

  8. [8]

    Imputing various incomplete attributes via distance likelihood maximization,

    S. Song and Y . Sun, “Imputing various incomplete attributes via distance likelihood maximization,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 535–545

    work page 2020

  9. [9]

    An introduction to modern missing data analyses,

    A. N. Baraldi and C. K. Enders, “An introduction to modern missing data analyses,” Journal of school psychology , vol. 48, no. 1, pp. 5–37, 2010

    work page 2010

  10. [10]

    Sice: an improved missing data imputation technique,

    S. I. Khan and A. S. M. L. Hoque, “Sice: an improved missing data imputation technique,” Journal of big Data , vol. 7, no. 1, p. 37, 2020

    work page 2020

  11. [11]

    R. J. Little and D. B. Rubin, Statistical analysis with missing data. John Wiley & Sons, 2019

    work page 2019

  12. [12]

    Gain: Missing data imputation using generative adversarial nets,

    J. Yoon, J. Jordon, and M. Schaar, “Gain: Missing data imputation using generative adversarial nets,” in International conference on machine learning. PMLR, 2018, pp. 5689–5698

    work page 2018

  13. [13]

    Can foundation models wrangle your data?

    A. Narayan, I. Chami, L. Orr, S. Arora, and C. R ´e, “Can foundation models wrangle your data?” arXiv preprint arXiv:2205.09911 , 2022

    work page arXiv 2022

  14. [14]

    Jellyfish: Instruction- tuning local large language models for data preprocessing,

    H. Zhang, Y . Dong, C. Xiao, and M. Oyamada, “Jellyfish: Instruction- tuning local large language models for data preprocessing,” in Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024, pp. 8754–8782

    work page 2024

  15. [15]

    Towards efficient data wrangling with llms using code generation,

    X. Li and T. D ¨ohmen, “Towards efficient data wrangling with llms using code generation,” in Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning , 2024, pp. 62–66

    work page 2024

  16. [16]

    Imputation of missing longitudinal data: a comparison of methods,

    J. M. Engels and P. Diehr, “Imputation of missing longitudinal data: a comparison of methods,” Journal of clinical epidemiology , vol. 56, no. 10, pp. 968–976, 2003

    work page 2003

  17. [17]

    Fallacies of last observation carried forward analyses,

    J. M. Lachin, “Fallacies of last observation carried forward analyses,” Clinical trials, vol. 13, no. 2, pp. 161–168, 2016

    work page 2016

  18. [18]

    Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial,

    V . Klamroth-Marganska, J. Blanco, K. Campen, A. Curt, V . Dietz, T. Ettlin, M. Felder, B. Fellinghauer, M. Guidali, A. Kollmar et al. , “Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial,” The Lancet Neurology , vol. 13, no. 2, pp. 159–166, 2014

    work page 2014

  19. [19]

    Does analysis using “last observation carried forward

    F. J. Molnar, B. Hutton, and D. Fergusson, “Does analysis using “last observation carried forward” introduce bias in dementia research?” Cmaj, vol. 179, no. 8, pp. 751–753, 2008

    work page 2008

  20. [20]

    Eracer: a database approach for statistical inference and data cleaning,

    C. Mayfield, J. Neville, and S. Prabhakar, “Eracer: a database approach for statistical inference and data cleaning,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010, pp. 75–86

    work page 2010

  21. [21]

    HoloClean: Holistic Data Repairs with Probabilistic Inference

    T. Rekatsinas, X. Chu, I. F. Ilyas, and C. R ´e, “Holoclean: Holistic data repairs with probabilistic inference,” arXiv preprint arXiv:1702.00820 , 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  22. [22]

    Attention-based learning for missing data imputation in holoclean,

    R. Wu, A. Zhang, I. Ilyas, and T. Rekatsinas, “Attention-based learning for missing data imputation in holoclean,” Proceedings of Machine Learning and Systems , vol. 2, pp. 307–325, 2020

    work page 2020

  23. [23]

    Exact matrix completion via convex opti- mization,

    E. Candes and B. Recht, “Exact matrix completion via convex opti- mization,” Communications of the ACM , vol. 55, no. 6, pp. 111–119, 2012

    work page 2012

  24. [24]

    Missforest—non-parametric missing value imputation for mixed-type data,

    D. J. Stekhoven and P. B ¨uhlmann, “Missforest—non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112–118, 2012

    work page 2012

  25. [25]

    Xgboost: A scalable tree boosting system,

    T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , 2016, pp. 785–794

    work page 2016

  26. [26]

    An introduction to kernel and nearest-neighbor non- parametric regression,

    N. S. Altman, “An introduction to kernel and nearest-neighbor non- parametric regression,” The American Statistician , vol. 46, no. 3, pp. 175–185, 1992

    work page 1992

  27. [27]

    Nearest neighbor ensemble,

    C. Domeniconi and B. Yan, “Nearest neighbor ensemble,” in Proceed- ings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., vol. 1. IEEE, 2004, pp. 228–231

    work page 2004

  28. [28]

    Missing categorical data imputation approach based on similarity,

    S. Wu, X. Feng, Y . Han, and Q. Wang, “Missing categorical data imputation approach based on similarity,” in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2012, pp. 2827–2832

    work page 2012

  29. [29]

    Missing value imputation based on gaussian mixture model for the internet of things,

    X. Yan, W. Xiong, L. Hu, F. Wang, and K. Zhao, “Missing value imputation based on gaussian mixture model for the internet of things,” Mathematical Problems in Engineering , vol. 2015, no. 1, p. 548605, 2015

    work page 2015

  30. [30]

    A new iterative fuzzy clustering algorithm for multiple imputation of missing data,

    S. Nikfalazar, C.-H. Yeh, S. Bedingfield, and H. A. Khorshidi, “A new iterative fuzzy clustering algorithm for multiple imputation of missing data,” in 2017 IEEE International Conference on Fuzzy Systems (FUZZ- IEEE). IEEE, 2017, pp. 1–6

    work page 2017

  31. [31]

    Mida: Multiple imputation using denoising autoencoders,

    L. Gondara and K. Wang, “Mida: Multiple imputation using denoising autoencoders,” in Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III 22. Springer, 2018, pp. 260–272

    work page 2018

  32. [32]

    Handling incomplete heterogeneous data using vaes,

    A. Nazabal, P. M. Olmos, Z. Ghahramani, and I. Valera, “Handling incomplete heterogeneous data using vaes,” Pattern Recognition , vol. 107, p. 107501, 2020

    work page 2020

  33. [33]

    Capturing semantics for imputation with pre-trained language models,

    Y . Mei, S. Song, C. Fang, H. Yang, J. Fang, and J. Long, “Capturing semantics for imputation with pre-trained language models,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE) . IEEE, 2021, pp. 61–72

    work page 2021

  34. [34]

    A hybrid approach to functional depen- dency discovery,

    T. Papenbrock and F. Naumann, “A hybrid approach to functional depen- dency discovery,” in Proceedings of the 2016 International Conference on Management of Data , 2016, pp. 821–833

    work page 2016

  35. [35]

    Conditional functional dependencies for data cleaning,

    P. Bohannon, W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis, “Conditional functional dependencies for data cleaning,” in 2007 IEEE 23rd international conference on data engineering . IEEE, 2006, pp. 746–755

    work page 2007

  36. [36]

    Fixing rules for data cleaning based on conditional functional dependency,

    R. Salem and A. Abdo, “Fixing rules for data cleaning based on conditional functional dependency,” Future Computing and Informatics Journal, vol. 1, no. 1-2, pp. 10–26, 2016

    work page 2016

  37. [37]

    Data repair of density-based data cleaning approach using conditional functional dependencies,

    S. Al-Janabi and R. Janicki, “Data repair of density-based data cleaning approach using conditional functional dependencies,” Data Technologies and Applications, vol. 56, no. 3, pp. 429–446, 2022

    work page 2022

  38. [38]

    Approximate inference of functional dependencies from relations,

    J. Kivinen and H. Mannila, “Approximate inference of functional dependencies from relations,” Theoretical Computer Science , vol. 149, no. 1, pp. 129–149, 1995

    work page 1995

  39. [39]

    Discovering reliable ap- proximate functional dependencies,

    P. Mandros, M. Boley, and J. Vreeken, “Discovering reliable ap- proximate functional dependencies,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 355–363

    work page 2017

  40. [40]

    Discovery of functional and approximate functional dependencies in relational databases,

    R. S. King and J. J. Legendre, “Discovery of functional and approximate functional dependencies in relational databases,” Journal of Applied Mathematics and Decision Sciences , vol. 7, no. 1, pp. 49–59, 2003

    work page 2003

  41. [41]

    Learning Functional Dependencies with Sparse Regression

    Z. Guo and T. Rekatsinas, “Learning functional dependencies with sparse regression,” arXiv preprint arXiv:1905.01425 , 2019

    work page internal anchor Pith review Pith/arXiv arXiv 1905

  42. [42]

    Dafdiscover: Robust mining algorithm for dynamic approximate functional depen- dencies on dirty data,

    X. Ding, Y . Lu, H. Wang, C. Wang, Y . Liu, and J. Wang, “Dafdiscover: Robust mining algorithm for dynamic approximate functional depen- dencies on dirty data,” Proceedings of the VLDB Endowment , vol. 17, no. 11, pp. 3484–3496, 2024

    work page 2024

  43. [43]

    Large language models are few (1)-shot table reasoners,

    W. Chen, “Large language models are few (1)-shot table reasoners,” arXiv preprint arXiv:2210.06710 , 2022

    work page arXiv 2022

  44. [44]

    Survey of hallucination in natural language generation,

    Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y . Xu, E. Ishii, Y . J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM computing surveys , vol. 55, no. 12, pp. 1–38, 2023

    work page 2023

  45. [45]

    On-line construction of suffix trees,

    E. Ukkonen, “On-line construction of suffix trees,” Algorithmica, vol. 14, no. 3, pp. 249–260, 1995

    work page 1995

  46. [46]

    Seed: Domain-specific data curation with large language models. arxiv 2023,

    Z. Chen, L. Cao, S. Madden, T. Kraska, Z. Shang, J. Fan, N. Tang, Z. Gu, C. Liu, and M. Cafarella, “Seed: Domain-specific data curation with large language models. arxiv 2023,” arXiv preprint arXiv:2310.00749 , 2024

    work page arXiv 2023

  47. [47]

    Baran: Effective error correction via a unified context representation and transfer learning,

    M. Mahdavi and Z. Abedjan, “Baran: Effective error correction via a unified context representation and transfer learning,” Proceedings of the VLDB Endowment, vol. 13, no. 12, pp. 1948–1961, 2020

    work page 1948