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arxiv: 2605.15467 · v1 · pith:CGHDYSAHnew · submitted 2026-05-14 · 💻 cs.CL · cs.AI

Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction

A H M Rezaul Karim , Ozlem Uzuner This is my paper

Pith reviewed 2026-05-19 14:33 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords retrieval-augmented generationclinical information extractionschema-constrained promptinglarge language modelsnurse-patient transcriptsstructured documentationobservation extractionschema adherence
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The pith

A retrieval-augmented pipeline with schema-constrained prompts extracts structured clinical observations from transcripts at 80.36 percent F1.

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

The paper introduces a modular retrieval-augmented generation pipeline to convert conversational nurse-patient transcripts into structured representations that follow a predefined schema with value-type constraints. It retrieves examples from the training set to guide prompting, tests full versus pruned schema details, applies deterministic postprocessing, and adds an optional second-pass audit. Results across two language model backbones show that retrieval improves outputs consistently, the preferred amount of schema information depends on the model, and auditing corrects some remaining adherence errors. This setup targets the practical problem of turning clinical dialogues into usable structured data without heavy manual review.

Core claim

The authors establish that combining retrieval from the training set as an exemplar corpus, schema-constrained prompting, deterministic schema-based postprocessing, and a second-pass audit enables large language models to produce schema-adherent structured outputs for observation extraction, with the strongest configuration reaching 80.36 percent F1.

What carries the argument

A modular retrieval-augmented generation pipeline that pulls similar training examples to inform prompts containing either the full schema or a pruned candidate schema, then applies deterministic postprocessing and auditing to enforce value-type constraints.

If this is right

  • Retrieval augmentation consistently improves performance on schema-constrained clinical extraction.
  • The optimal degree of schema detail in prompts depends on the specific language model.
  • Second-pass auditing yields modest additional gains by correcting residual schema-adherence errors.
  • Using the training set as an exemplar corpus supports better adherence in generated outputs.

Where Pith is reading between the lines

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

  • The pipeline could reduce clinician documentation time by turning spoken interactions into structured records automatically.
  • Similar retrieval-plus-constraint methods might apply to other structured output tasks outside clinical conversations.
  • Dynamic adjustment of schema pruning based on model size could yield further efficiency gains.

Load-bearing premise

The training set can serve as an effective exemplar corpus for retrieval that meaningfully improves the model's ability to produce schema-adherent outputs when combined with prompting and post-processing.

What would settle it

Replacing the training-set retrieval step with random or unrelated examples and finding no drop in F1 score or schema adherence would indicate that the RAG component is not contributing as claimed.

Figures

Figures reproduced from arXiv: 2605.15467 by A H M Rezaul Karim, Ozlem Uzuner.

Figure 1
Figure 1. Figure 1: Retrieval-augmented, schema-constrained pipeline that retrieves training exemplars, conditions [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The structured prompt with retrieved ex [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The structured prompt for the second pass audit with retrieved exemplars, schema, first pass solution, and the expected output. mission format by retrieving the standard name and value_type from the schema, producing id, name, value_type, value entries. 4.7. Second-Pass Auditing [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Conversational nurse-patient transcripts contain actionable observations, but converting these transcripts into structured representations at scale remains challenging. Documentation burden is substantial, with prior studies showing clinicians spend large portions of their workday on documentation and related desk work rather than direct patient care. MEDIQA-SYNUR focuses on observation extraction from conversational nurse-patient transcripts, requiring systems to normalize these narratives into a predefined schema with value-type constraints. We propose a modular retrieval-augmented generation (RAG) pipeline that uses the training set as an exemplar corpus, combines schema-constrained prompting (full schema vs. pruned candidate schema), deterministic schema-based postprocessing, and a second-pass audit, with two LLM backbones: Llama-4-Scout-17B-16E-Instruct and GPT-5.2 with corresponding embedding models for RAG. Our best configuration uses GPT-5.2 with full schema, RAG, and a second-pass auditing, achieving 80.36% F1 score. Overall, our results show that RAG consistently improves performance, while the optimal degree of schema constraint depends on the model, and second-pass auditing yields modest additional gains by correcting residual schema-adherence errors.

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

2 major / 1 minor

Summary. The manuscript describes a retrieval-augmented generation (RAG) pipeline for schema-constrained extraction of clinical observations from conversational nurse-patient transcripts. It uses the training set as an exemplar corpus for retrieval, compares full versus pruned schema prompting, applies deterministic schema-based post-processing, and optionally adds a second-pass audit. Experiments with Llama-4-Scout-17B-16E-Instruct and GPT-5.2 report that the best configuration (GPT-5.2 + full schema + RAG + audit) reaches 80.36% F1, with the overall finding that RAG improves performance, optimal schema constraint is model-dependent, and auditing yields modest gains by fixing residual adherence errors.

Significance. If the reported gains are confirmed by isolating the retrieval contribution and providing standard evaluation details, the work would illustrate a practical modular strategy for improving LLM schema adherence in clinical information extraction. This could have applied value for reducing documentation burden in healthcare by structuring conversational data more reliably. The use of both open-weight and proprietary models plus explicit comparison of prompting variants is a constructive aspect of the design.

major comments (2)
  1. Abstract and Results: The headline performance claim of 80.36% F1 and the statement that 'RAG consistently improves performance' are presented without baseline comparisons, statistical significance tests, error analysis, dataset split details, or explicit protocols for measuring schema violations, preventing full assessment of the empirical support.
  2. Experimental Setup / Results: The central assertion that retrieval from the training-set exemplar corpus drives the observed gains rests on an untested premise; no ablation is reported that holds schema prompting, post-processing, and auditing fixed while varying only the retrieval source (nearest-neighbor vs. random training examples vs. none) to isolate whether gains arise from retrieval quality rather than prompt length, ordering, or deterministic post-processing.
minor comments (1)
  1. Abstract: The distinction between 'full schema' and 'pruned candidate schema' would be clearer if accompanied by a short illustrative example or pointer to a table/figure in the main text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the potential applied value of our modular RAG pipeline for schema-constrained clinical extraction. We address the two major comments below and indicate the revisions we will make to strengthen the empirical support and experimental design.

read point-by-point responses

  1. Referee: Abstract and Results: The headline performance claim of 80.36% F1 and the statement that 'RAG consistently improves performance' are presented without baseline comparisons, statistical significance tests, error analysis, dataset split details, or explicit protocols for measuring schema violations, preventing full assessment of the empirical support.

    Authors: We agree that additional detail is required for readers to fully evaluate the reported results. In the revised manuscript we will (1) add explicit baseline comparisons (zero-shot and few-shot prompting without retrieval), (2) report statistical significance using bootstrap resampling or McNemar’s test across multiple runs, (3) include a dedicated error-analysis subsection that categorizes remaining failures, (4) state the exact train/validation/test splits used from MEDIQA-SYNUR, and (5) describe the deterministic protocol employed to detect and count schema violations. These changes will be reflected in both the abstract and the results section. revision: yes

  2. Referee: Experimental Setup / Results: The central assertion that retrieval from the training-set exemplar corpus drives the observed gains rests on an untested premise; no ablation is reported that holds schema prompting, post-processing, and auditing fixed while varying only the retrieval source (nearest-neighbor vs. random training examples vs. none) to isolate whether gains arise from retrieval quality rather than prompt length, ordering, or deterministic post-processing.

    Authors: We acknowledge that a finer-grained ablation isolating retrieval quality would strengthen the causal claim. While the current experiments already compare RAG against a no-retrieval condition with all other components fixed, we did not test random example selection. In the revision we will add this ablation: nearest-neighbor retrieval, random selection from the training corpus, and no retrieval, keeping schema prompting, post-processing, and auditing identical. Results will be reported for both model backbones to clarify the contribution of retrieval relevance versus prompt length or ordering. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical pipeline

full rationale

The paper reports measured F1 scores from an experimental RAG pipeline on held-out evaluation data. Performance claims rest on direct comparison of configurations (with/without RAG, full vs. pruned schema, with/without audit) rather than any derivation that reduces to its own inputs by construction. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the load-bearing steps. The training-set exemplars are an explicit design choice whose contribution is presented as testable via ablation, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the premise that retrieved training examples plus schema prompting and rule-based cleanup can reliably produce outputs that satisfy a predefined clinical schema; this is treated as an empirical engineering assumption rather than a derived result.

axioms (2)
  • domain assumption Large language models can be guided by retrieved exemplars and schema descriptions to produce outputs that largely respect value-type constraints.
    Invoked to justify the schema-constrained prompting and RAG components of the pipeline.
  • domain assumption Deterministic post-processing and a second-pass audit can correct residual schema-adherence errors left by the model.
    Invoked to justify the modular post-processing and auditing steps.

pith-pipeline@v0.9.0 · 5740 in / 1529 out tokens · 89332 ms · 2026-05-19T14:33:37.610108+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

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  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
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    Relation between the paper passage and the cited Recognition theorem.

    modular retrieval-augmented generation (RAG) pipeline that uses the training set as an exemplar corpus, combines schema-constrained prompting (full schema vs. pruned candidate schema), deterministic schema-based postprocessing, and a second-pass audit

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Reference graph

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