Pith. sign in

REVIEW 7 cited by

Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2310.10358 v1 pith:JYVYVCRU submitted 2023-10-16 cs.CL cs.AI

Ananya Singha , José Cambronero , Sumit Gulwani , Vu Le , Chris Parnin This is my paper
classification cs.CLcs.AI
keywords tabletasksllmsformatsinspiredoperationsperformancerepresentation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of self-supervised structural tasks (e.g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using 8 formats. In contrast to past work, we introduce 8 noise operations inspired by real-world messy data and adversarial inputs, and show that such operations can impact LLM performance across formats for different structural understanding tasks.

Discussion (0). Sign in to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Effect of Scripts and Formats on LLM Numeracy

    cs.CL 2026-01 conditional novelty 6.0 of 10

    LLM arithmetic accuracy falls sharply when numerals leave the familiar Hindu–Arabic format, and few-shot prompting with examples narrows most of that gap.

  2. Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches

    cs.LG 2025-08 conditional novelty 6.0 of 10

    Serialization format and in-context examples change both accuracy and gender fairness of LLM loan approvals, with finance-tuned models often showing larger disparities.

  3. Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs

    cs.CR 2025-07 conditional novelty 6.0 of 10

    Tab-MIA shows LLMs fine-tuned on tabular data are vulnerable to membership inference attacks, with AUROC up to 97.7% after three epochs and encoding format strongly affecting leakage.

  4. Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new benchmark, TableEval, with 3017 tables in five formats, shows LLMs are robust to table representation but perform worse on scientific tables, with the caveat that the domain gap is confounded by task difficulty.

  5. Large Language Models are Good Relational Learners

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Rel-LLM combines a GNN encoder with a frozen LLM via soft prompts and masked attribute pretraining, reporting improved average performance on RelBench relational database tasks.

  6. Towards Scalable Schema Mapping using Large Language Models

    cs.DB 2025-05 conditional novelty 5.0 of 10

    LLM-based schema mapping can be made more scalable and robust through sampled prompts, bidirectional confidence aggregation, and rule chunking, letting a smaller open-source model match GPT-4-based performance on MIMI...

  7. The Prompt is Mightier than the Example

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Injecting domain knowledge into prompts can substitute for many in-context examples in LLM-based synthetic tabular data generation, cutting required example counts by 40-90%.

Pith tools