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REVIEW 4 major objections 5 minor 39 references

Infusing Prompts with Syntax and Semantics

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Appending serialized dependency trees and AMR graphs to prompts improves multilingual text-to-SQL and surpasses prior state-of-the-art results on Portuguese and French.

desk verdict Plausible empirical result on prompt-infused syntax and AMR for multilingual NL2SQL, but the causal attribution to linguistic structure is not yet established because the cross-lingual parser output is unvalidated and no non-linguistic control is run. read the letter →

arxiv 2412.06107 v1 pith:EMREZ4FY submitted 2024-12-08 cs.CL

Anton Bulle Labate , Fabio Gagliardi Cozman This is my paper · ORCID
classification cs.CL
keywords text-to-SQLNL2SQLpromptinfusiondependencyparsingAbstractMeaningRepresentationlow-resourcelanguagesSpiderbenchmarkmultilinguallanguagemodels
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

This paper argues that appending cheap linguistic analyses to prompts can substantially improve how language models translate natural-language questions into SQL, especially for languages with fewer training resources than English. The authors concatenate a filtered dependency-tree serialization and a linearized Abstract Meaning Representation (AMR) graph to each input question, without altering the model architecture. On translated versions of the Spider benchmark, they report consistent gains in exact-set match and execution accuracy for French, Spanish, Portuguese, and Chinese, with AMR infusion usually the strongest single signal. Their best T5-based models surpass the previously reported state of the art for Portuguese and French. The same infusion gives little or no benefit on English, which the paper attributes to large models already absorbing the relevant linguistic information during pretraining.

What carries the argument

The mechanism is prompt-level infusion: instead of modifying attention or embeddings, the authors serialize the output of off-the-shelf parsers and concatenate it with the question before the transformer sees it. For syntax, they take dependency trees and keep only subject, object, and conjunction relations, rendered as '[row] head; relation [row] dependent; ...' using the serialization of Li et al. (2021). For semantics, they linearize Abstract Meaning Representation (AMR) graphs—directed acyclic graphs whose nodes are language-independent concepts and edges are semantic roles—into bracket notation such as '(l / list-01 :ARG1 ...)', tagging it with '[AMR]'. The resulting prompt is what trains and, optionally, prompts the model; no component of the underlying NL2SQL framework is changed.

What would settle it

A control experiment that replaces each AMR serialization with a scrambled or randomly generated bracketed string of the same length would settle the semantic claim: if scrambled graphs preserve the accuracy gains, the semantic content is not what is doing the work.

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Extended reading notes

Core claim

The discovery is that explicit syntactic and semantic structure, written into the prompt as plain text, helps sequence-to-sequence models do better at NL2SQL in low-resource languages. Dependency relations (subjects, objects, conjunctions) are serialized as '[row] head; dobj [row] dependent; ...' following the format of Li et al. (2021), and AMR graphs are flattened with '[AMR]' as a separator. The enlarged prompt is fed into T5 or BART models inside a schema-pruning, skeleton-decoding NL2SQL pipeline, which first selects relevant tables and columns and then decodes the SQL skeleton before filling in values. Across four non-English languages and two model families, every infused variant outperformed the corresponding raw-input model; AMR alone was often best, and syntax plus AMR was best for BART and Chinese. The paper also reports that infused models reach target accuracy with fewer epochs than uninfused models, and that a 3-billion-parameter T5 using the flattened SQL intermediate representation of Gan et al. (2021) exceeds the previous reported exact-set-match score for Portuguese (0.752 vs. 0.687) and French (0.749 vs. 0.698).

Load-bearing premise

The paper's gains rest on the assumption that the English-oriented semantic parser it uses produces meaningful AMR graphs for French, Spanish, Portuguese, and Chinese input, and that any observed improvement comes from that semantic signal rather than from added tokens or noise.

Editorial extensions

If this is right

  • For French, Spanish, Portuguese, and Chinese, every infused model family (T5, BART, and mT5) outperformed its raw-input counterpart on exact-set match and execution accuracy.
  • AMR infusion alone is the strongest single signal in most comparisons, and combining syntax with AMR is best for BART and for Chinese mT5.
  • Models trained with infused prompts for 32 epochs reach accuracies close to those of uninfused models trained for 128 epochs, so the linguistic signal cuts training cost.
  • On English Spider the same infusion produces little or negative change, indicating the benefit concentrates in low-resource settings.

Reading between the lines

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

  • A testable extension is to apply the same serialized-parse and AMR prompt pattern to other relation-sensitive generation tasks, such as event extraction or structured summarization, since the pipeline does not depend on SQL.
  • The English results suggest a resource-volume crossover: as pretraining data for a language grows, explicit linguistic annotation matters less; locating that crossover would tell practitioners when parser costs are worth paying.
  • Because the semantic graphs come from an English-oriented parser, the reported AMR gains for Romance languages and Chinese could partly reflect added structure rather than faithful meaning; a study using gold multilingual AMR graphs would isolate the semantic contribution.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes to improve NL2SQL for low-resource languages by appending serialized dependency trees and AMR graphs to the input prompts of sequence-to-sequence models. Using translated versions of Spider in French, Spanish, Portuguese, and Chinese, the authors fine-tune T5, BART, and mT5 with and without the infused linguistic information, reporting consistent improvements in exact-set match and execution accuracy, faster convergence (32 vs. 128 epochs), and new state-of-the-art results for Portuguese and French. The manuscript also includes an English control experiment and a manual evaluation of the Portuguese translation quality.

Significance. If the results hold, the contribution is a simple, architecture-agnostic prompt augmentation that improves low-resource semantic parsing and reduces training cost. The paper's strengths are its comparison across four languages, two base architectures, and two training budgets; the appended translation-quality evaluation; and the honest negative result on English. Its principal weakness is that the semantic component is not validated for the target languages: amrlib is English-oriented, and no control rules out the possibility that the gains come from added structured tokens or English cues rather than from meaningful AMR. The absence of error bars and single-run reporting also leaves the magnitude of the gains uncertain.

major comments (4)
  1. [Section 4, paragraph on dependency trees and AMR graphs; Tables 1-2] The AMR parser amrlib is trained on English AMR, and the paper provides no evidence that its graphs are meaningful for French, Spanish, Portuguese, or Chinese. Because AMR alone gives the largest gains in most rows of Tables 1 and 2, the central claim that semantic information causes the improvement is not established. Please add (a) a sample of AMR outputs for each language with human or intrinsic evaluation, and (b) a control experiment with scrambled or nonce AMR serializations (or a random graph baseline) to show that the linearized graph content, not the extra bracket/token structure, is responsible for the gains.
  2. [Section 3, running example and Figure 1] The dependency-tree prompt shown for the French example uses English lexical items ('year', 'name', 'budget', 'department') even though the dependency tree displayed immediately above is in French ('année', 'nom', 'budget', 'département'). If this reflects the actual prompt construction, the 'with syntax' condition also changes the language mix relative to the 'without info' baseline, conflating structural information with English token injection. Please clarify how the dependency-to-text conversion maps tokens and, if English is introduced, ablate it separately.
  3. [Tables 1-3 and Section 4] All experiments appear to be single runs with no error bars, confidence intervals, or significance tests. Many reported differences are small (e.g., French T5 with syntax vs. without info at 32 epochs is a 1.1-point EM difference in Table 1 top), so the consistency of the improvement cannot be assessed. Please report multiple seeds with means and standard deviations, or paired tests, at least for the main comparisons.
  4. [Table 4 and Section 4] The claim of surpassing the previous state of the art is based on a comparison with the authors' own prior system (Jose and Cozman 2023), for which no execution accuracy is available, and no details are given for the T5-3B plus NatSQL setup (e.g., whether it uses RESDSQL, which dev-set split, how many runs). Please specify the comparison protocol and, ideally, compare against an external published multilingual NL2SQL system on the same dev sets.
minor comments (5)
  1. [Section 3, Figure 1] The figure labels the input side 'RoBERTa' while the generation models in the experiments are T5 or BART; clarify the role of RoBERTa in the RESDSQL schema-linking stage.
  2. [Section 4 and Tables 1-2] The vEM and vEX columns are said to be relative variations but the formula is not given, and the captions do not state that the values are percentages; please add the definition and units.
  3. [Section 4] The sentence 'Our code and resulting models will be made publicly available in case the present text is accepted' should be replaced with a concrete availability statement (repository, license, model links) in the accepted version.
  4. [References and Section 2] There are spacing artifacts in references (e.g., 'Y aghmazadeh', 'V Y') and a duplicated 'and' in the sentence describing Li et al.; please proofread the final text.
  5. [Section 3, AMR example] The linearized AMR is written with spaces in 'list - 01' and 'year :time - of'; standard AMR notation has no such spaces, and the paper should ensure the shown linearization is exactly what is fed to the model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central claim rests on controlled empirical contrasts; the only self-citations are published datasets/baselines that are not load-bearing.

full rationale

The paper's central claim is supported by controlled comparisons: for each language, model, and epoch count, the syntax/AMR-infused condition is compared against the same architecture trained on the raw translated Spider input (Tables 1-3). The infusion strings are produced from the input question by external parsers (Spacy and amrlib), not from the target SQL, so the experimental manipulation is not defined in terms of the dependent variable. No parameter is fitted to the dev set and then reported as a prediction; the vEM/vEX columns are simply relative differences computed on the same held-out dev split across conditions. The only self-citations are the Jose and Cozman (2023) translated datasets and the previous French/Portuguese baseline used in Table 4; that baseline is a published, externally checkable result, and the translation quality is independently audited in the Appendix, so the comparison does not reduce to an unsupported self-assertion. The English experiments (Table 3) serve as a negative control and show the expected null or degraded effect, further indicating that the non-English gains are not an artifact of the prompting format alone. Concerns about amrlib producing English-oriented AMR graphs for non-English input are construct-validity issues that could challenge the causal attribution, but they do not exhibit the reduction of a result to its own inputs by definition or by fitted equivalence. Therefore no circular step is identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new theoretical entities or fitted parameters. Its load-bearing assumptions are the reliability of the parsing tools for the target languages and the quality of the translated datasets. The most fragile of these is the cross-lingual validity of amrlib, which the paper does not address.

assumptions (3)
  • domain assumption SpaCy and amrlib produce correct dependency and AMR structures for French, Spanish, Portuguese, and Chinese sentences.
    Section 4 states the parsers generate the structures used in prompts; no validation is provided for amrlib on non-English input, while amrlib is designed for English.
  • domain assumption The translated Spider datasets used for training and evaluation are of sufficient quality to draw conclusions.
    The authors evaluate only the Portuguese translation (Section A) and note that all models share the same translation, so relative comparisons hold, but absolute performance may be affected by translation errors.
  • domain assumption The development set is a valid proxy for test performance.
    Section 4 states the test set is not publicly available, so validation uses the dev set only.

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Cite this review

Pith. "Pith review of Infusing Prompts with Syntax and Semantics." pith.science (2026). https://pith.science/paper/EMREZ4FY

@misc{pith2026241206107,
  author       = {Pith},
  title        = {Pith review of: Infusing Prompts with Syntax and Semantics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EMREZ4FY}},
  note         = {Machine review of arXiv:2412.06107}
}
read the original abstract

Despite impressive success, language models often generate outputs with flawed linguistic structure. We analyze the effect of directly infusing various kinds of syntactic and semantic information into large language models. To demonstrate the value of our proposals, we focus on the translation of natural language queries to SQL, in particular dealing with languages with less resources than English, to better investigate how much help we can get from low cost syntactic and semantic information. We show that linguistic analysis can significantly boost language models, to the point that we have surpassed previous best systems.

Figures

Figures reproduced from arXiv: 2412.06107 by the authors.

Figure 1
Figure 1. The pipeline, from input to output. tion) during inference. Thus we do not have to change the architecture of the LM of choice. The resulting pipeline is depicted in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

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