Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents

Haifeng Chen; Haoyu Wang; Kaiqiao Han; LuAn Tang; Peng Yuan; Renliang Sun; Wei Cheng; Wei Wang; Yizhou Sun

REVIEW 1 major objections 1 minor 17 references

Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents

T0 review · 1 major / 1 minor · reviewed 2026-06-30 · grok-4.3

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

Pith's one-line read Tables serve as an intermediate structured representation that reduces noise in retrieval-augmented generation while preserving essential information from noisy contexts.

desk verdict SA-RAG stacks tables, metadata generation, and DPO for noisy RAG but the experiments do not isolate what the tables themselves contribute. read the letter →

arxiv 2605.24366 v1 pith:5LILVGMB submitted 2026-05-23 cs.CL cs.LG

Kaiqiao Han , LuAn Tang , Renliang Sun , Peng Yuan , Wei Cheng , Haoyu Wang , Wei Wang , Yizhou Sun

show 1 more author

Haifeng Chen

This is my paper

classification cs.CLcs.LG

keywords retrievalaugmentedgenerationstructuredtablesnoisydataconversationalagentsmetadataLLMreliabilitydirectpreferenceoptimization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes SA-RAG to address how standard text-based and graph-based retrieval-augmented generation methods falter when external knowledge contains noise or irrelevancies. It introduces tables as a compact, controllable intermediate layer between raw retrieval and LLM generation. A quality-aware metadata generation framework, along with training-free and training-based table creation methods plus validation and preference optimization, is used to produce those tables. Experiments on two noisy real-world datasets demonstrate that the resulting system outperforms existing RAG baselines for conversational agents. A sympathetic reader would care because reliable dynamic or domain-specific knowledge remains essential for practical LLM deployment.

What carries the argument

tables as an intermediate structured representation, supported by a quality-aware table metadata generation framework

What would settle it

An experiment on the same two noisy real-world datasets that finds SA-RAG accuracy no higher than standard text or graph RAG baselines.

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Extended reading notes

Core claim

SA-RAG uses tables as an intermediate structured representation to provide a compact and controllable interface that reduces noise while preserving essential information. A quality-aware table metadata generation framework models metadata normalization and effectiveness to improve quality and downstream performance. Both training-free and training-based table generation methods are explored, with generation validation and direct preference optimization further refining table quality while maintaining semantic and structural consistency.

Load-bearing premise

That tables as an intermediate structured representation provide a compact and controllable interface that reduces noise while preserving essential information from the original noisy contexts.

Editorial extensions

If this is right

SA-RAG significantly outperforms existing RAG baselines on noisy real-world datasets for conversational agents.
Modeling metadata normalization and effectiveness improves both metadata quality and final generation performance.
Generation validation combined with direct preference optimization raises table quality without breaking semantic or structural consistency.
Both training-free and training-based table generation routes remain viable under the SA-RAG pipeline.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The table interface may generalize to other structured formats such as JSON schemas or entity-relation graphs when noise patterns differ.
Domain-specific fine-tuning of the metadata generation step could further tighten the controllable interface for specialized conversational tasks.
If table construction cost scales linearly with context length, the method may favor shorter retrieval windows over larger noisy passages.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

This paper introduces SA-RAG, which turns retrieved text into tables as an intermediate representation to reduce noise in RAG for conversational agents, plus a quality-aware metadata pipeline and direct preference optimization. The central claim is that this beats standard RAG baselines on two noisy real-world datasets.

The new pieces are the metadata framework that models normalization and effectiveness, the comparison of training-free and training-based table generation, and the validation plus DPO step to maintain consistency. These are presented as practical additions rather than routine extensions.

The work does a reasonable job targeting a real deployment issue and releasing code, which lets others inspect the pipeline.

The soft spot is the missing isolation. The gains are reported for the full stack, but nothing described holds metadata, generation method, and optimization fixed while varying only the table format versus raw text. Without that, it is hard to credit the structure itself for the noise reduction. The assumption that tables give a compact controllable interface is reasonable on its face but not directly tested in the results given.

This is for engineers already running RAG systems on messy data who want a concrete pipeline to try. It will not shift the broader field.

I would send it for peer review. The datasets are real and the setup is specific enough that referees can check the numbers and request the needed ablations.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes Structure-Aware RAG (SA-RAG) for conversational agents, which converts noisy input contexts into tables as an intermediate structured representation to reduce noise while preserving essential information. It introduces a quality-aware table metadata generation framework, compares training-free and training-based table generation methods, applies generation validation and direct preference optimization (DPO), and reports that SA-RAG significantly outperforms existing text-based and graph-based RAG baselines on two noisy real-world datasets. The code is released publicly.

Significance. If the performance gains can be shown to stem specifically from the tabular representation after appropriate controls, the work could provide a practical interface for handling noisy data in RAG systems. The public code release supports reproducibility and is a clear strength.

major comments (1)

[Experiments] The central claim that tables provide a compact and controllable interface reducing noise (abstract and introduction) is load-bearing for the contribution, yet the experimental evaluation does not isolate this factor. No ablation holds the metadata framework, generation method, validation, and DPO fixed while varying only table-based versus raw-text retrieval; reported gains on the two datasets could therefore arise from any of the stacked interventions rather than the tabular format itself.

minor comments (1)

[Abstract] The abstract asserts significant outperformance but supplies no metrics, baselines, or statistical details; moving a concise summary of the evaluation protocol and key numbers into the abstract would improve readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed review and constructive feedback. We address the major comment on experimental isolation below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [Experiments] The central claim that tables provide a compact and controllable interface reducing noise (abstract and introduction) is load-bearing for the contribution, yet the experimental evaluation does not isolate this factor. No ablation holds the metadata framework, generation method, validation, and DPO fixed while varying only table-based versus raw-text retrieval; reported gains on the two datasets could therefore arise from any of the stacked interventions rather than the tabular format itself.

Authors: We agree with the referee that isolating the effect of the tabular representation is crucial for substantiating our central claim. The current evaluation compares the complete SA-RAG system against standard text-based and graph-based RAG baselines, but does not include a controlled ablation that keeps the metadata framework, generation methods, validation, and DPO fixed while varying only the use of tables versus raw text. In the revised version, we will add such an ablation study. This will involve designing a comparable text-based pipeline that incorporates similar quality-aware processing and optimization steps to the extent possible, allowing us to more directly attribute performance gains to the structured table format. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; empirical claims rest on external dataset comparisons

full rationale

The paper advances SA-RAG as a method that inserts tables as an intermediate representation, augmented by metadata generation, training-free/training-based generation, validation, and DPO. Its central claim is that this yields better performance than prior RAG baselines on two noisy real-world datasets. No equations, fitted parameters, uniqueness theorems, or self-citations appear in the provided text that would reduce any prediction or result to an input by construction. The reported gains are presented as outcomes of experimental comparisons against external baselines, making the derivation self-contained against those benchmarks.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Review performed on abstract only; no specific free parameters, axioms, or invented entities beyond the high-level proposal of tables and metadata framework can be extracted.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents." pith.science (2026). https://pith.science/paper/5LILVGMB

@misc{pith2026260524366,
  author       = {Pith},
  title        = {Pith review of: Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5LILVGMB}},
  note         = {Machine review of arXiv:2605.24366}
}

read the original abstract

Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability in real-world scenarios that require dynamic or domain-specific information. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge during generation, but existing text-based and graph-based RAG methods often struggle with noisy or irrelevant contexts. In this work, we propose Structure-aware Retrieval Augmented Generation (SA-RAG), which uses tables as an intermediate structured representation to provide a compact and controllable interface that reduces noise while preserving essential information. We introduce a quality-aware table metadata generation framework that models metadata normalization and effectiveness, improving metadata quality and downstream performance. Furthermore, we explore both training-free and training-based table generation methods. Generation validation and direct preference optimization further improve table quality while maintaining semantic and structural consistency. Experiments on two noisy real-world datasets show that SA-RAG significantly outperforms existing RAG baselines. Our code is publicly available at a public repository.

Figures

Figures reproduced from arXiv: 2605.24366 by the authors.

**Figure 2.** Model Overview. Given a user query and noisy multi-turn conversational history, Structure-Aware [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Comparison of Table Quality Metrics Across [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

[1]

Florin Cuconasu, Giovanni Trappolini, Federico Sicil- iano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri

From retrieval to response: Tracing the impact of embedding quality in rag systems.IEEE Access, 13:212773–212781. Florin Cuconasu, Giovanni Trappolini, Federico Sicil- iano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri

work page
[2]

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

The power of noise: Redefining retrieval for rag systems. InProceedings of the 47th International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval, pages 719–729. Brad Eden. 2002. Metadata and its application.Library technology reports, 38(5):1–78. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Stev...

work page Pith review arXiv 2002
[3]

Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses

Tapas: Weakly supervised table parsing via pre-training. InProceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, page 4320–4333. Association for Computational Lin- guistics. Xanh Ho, Jiahao Huang, Florian Boudin, and Akiko Aizawa. 2025. Llm-as-a-judge: Reassessing the performance of llms in extractive qa.Preprint, arXi...

work page Pith review arXiv 2025
[4]

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Direct preference optimization: Your lan- guage model is secretly a reward model.Preprint, arXiv:2305.18290. Raghu Ramakrishnan, Johannes Gehrke, and Johannes Gehrke. 2003.Database management systems, vol- ume 3. McGraw-Hill New York. Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015. Yara parser: A fast and accurate dependency parser. Computing Researc...

work page Pith review arXiv 2003
[5]

table_name

Metadata effectiveness for downstream tasks: • For each column, we attempt to retrieve relevant information given sample user queries from the dialogue. • If the column allows successful retrieval and provide useful information, the col- umn is considered effective. 3.Value-level constraint satisfaction: • We define constraints for each column, such as ty...

work page 2018
[6]

relevance: How directly the column relates to the problem

work page
[7]

answerability: Contribution to answer- ing the problem

work page
[8]

Output: For each column, add field quality_score with relevance, answer- ability, overall, and justification

overall: Holistic judgment for this problem. Output: For each column, add field quality_score with relevance, answer- ability, overall, and justification. Return valid JSON only. CURRENT META DATA: {cur- rent_meta_data} NEW SCHEMA TO EV ALUATE: {new_schema} PROBLEM / QUERY: {problem} Prompt 4: Schema Governance and Merge You are a schema governance and ev...

work page

Show all 17 references

[9]

Decide if the new schema is accept- able
[10]

Detect semantic overlap with existing schemas
[11]

For each overlap, choose one opera- tion: ADD, UPDATE, MERGE, KEEP, DELETE
[12]

Produce the FINAL updated meta-data (same format as current meta-data, without quality scores)
[13]

row": {

Ensure no more than 20 columns. Decision rules: • Use column-level qual- ity_score.overall as primary signal. • Prefer schemas with higher average column quality, better coverage, and clearer semantics. • Delete only strictly worse redundant columns. • Avoid introducing new ta...

2018
[14]

Random field drop: randomly set some fields tonullto simulate missing values
[15]

Dialogue-based hallucination: replace some field values with randomly sampled words from the dialogue to produce realistic but in- correct entries
[16]

Field swapping: swap values across columns to create structural misalignment
[17]

drop", "hallucinate

Dialogue-inconsistent fill: fill fields with ran- domly selected sentences from the dialogue, teaching the model that direct copying of dia- logue may be incorrect. Combination of perturbationsFor diversity, one or multiple perturbations are randomly applied per table instance...

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[1] [1]

Florin Cuconasu, Giovanni Trappolini, Federico Sicil- iano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri

From retrieval to response: Tracing the impact of embedding quality in rag systems.IEEE Access, 13:212773–212781. Florin Cuconasu, Giovanni Trappolini, Federico Sicil- iano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri

work page

[2] [2]

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

The power of noise: Redefining retrieval for rag systems. InProceedings of the 47th International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval, pages 719–729. Brad Eden. 2002. Metadata and its application.Library technology reports, 38(5):1–78. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Stev...

work page Pith review arXiv 2002

[3] [3]

Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses

Tapas: Weakly supervised table parsing via pre-training. InProceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, page 4320–4333. Association for Computational Lin- guistics. Xanh Ho, Jiahao Huang, Florian Boudin, and Akiko Aizawa. 2025. Llm-as-a-judge: Reassessing the performance of llms in extractive qa.Preprint, arXi...

work page Pith review arXiv 2025

[4] [4]

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Direct preference optimization: Your lan- guage model is secretly a reward model.Preprint, arXiv:2305.18290. Raghu Ramakrishnan, Johannes Gehrke, and Johannes Gehrke. 2003.Database management systems, vol- ume 3. McGraw-Hill New York. Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015. Yara parser: A fast and accurate dependency parser. Computing Researc...

work page Pith review arXiv 2003

[5] [5]

table_name

Metadata effectiveness for downstream tasks: • For each column, we attempt to retrieve relevant information given sample user queries from the dialogue. • If the column allows successful retrieval and provide useful information, the col- umn is considered effective. 3.Value-level constraint satisfaction: • We define constraints for each column, such as ty...

work page 2018

[6] [6]

relevance: How directly the column relates to the problem

work page

[7] [7]

answerability: Contribution to answer- ing the problem

work page

[8] [8]

Output: For each column, add field quality_score with relevance, answer- ability, overall, and justification

overall: Holistic judgment for this problem. Output: For each column, add field quality_score with relevance, answer- ability, overall, and justification. Return valid JSON only. CURRENT META DATA: {cur- rent_meta_data} NEW SCHEMA TO EV ALUATE: {new_schema} PROBLEM / QUERY: {problem} Prompt 4: Schema Governance and Merge You are a schema governance and ev...

work page