Pith. sign in

REVIEW 3 major objections 5 minor 37 references

From Research Questions to Columns: Operationalization-Aware Data Discovery

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper defines operationalization-aware data discovery, builds a benchmark from published studies, and shows that current retrieval and schema-linking methods cover under half of the correct measurement columns.

desk verdict A useful new task and benchmark, but the recall metric against a single published operationalization overstates the 'systems fail' conclusion. read the letter →

arxiv 2608.04536 v1 pith:S52E3TUQ submitted 2026-08-05 cs.DB

Houming Chen , H. V. Jagadish This is my paper · ORCID
classification cs.DB
keywords operationalization-awaredatadiscoverycolumnretrievalbenchmarkconstructionschemalinkinglargelanguagemodelsHealthandRetirementStudymeasurementbundlesexact-columnrecall
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

Researchers often approach a data repository with a broad question, such as whether social isolation affects healthy aging, and need columns that can measure that concept. The paper argues that useful columns usually do not resemble the question, and that the real task is operationalization: choosing which concrete fields, often in combination, provide a defensible measure. It defines this as operationalization-aware data discovery (OADD) and builds OADD-Bench, a benchmark of 160 questions and 4,682 question–column labels mined from 111 published studies of a large longitudinal survey, treating each paper as a record of how researchers actually measured their concepts. On this benchmark, direct retrieval reaches at most 0.185 recall, the strongest schema-linking adaptation reaches 0.401, and the best OADD-directed agent reaches 0.465, so the problem remains open.

What carries the argument

The 'operationalization gap' is the central object: the logical distance between a broad concept and the concrete observations that can measure it, where a useful column may matter only as part of a bundle. The paper's construction pipeline is the machinery that makes the gap measurable: a question miner reframes paper questions without revealing measurement details, a paper-conditioned column miner reconstructs measurements through coarse-to-fine retrieval, evidence-packet screening, and a grounding-or-abstain final pass, and deterministic checks reject unsupported identifiers. Evaluation then converts the gap into a score using exact-column recall at output budgets of one, two, and five times the target size.

What would settle it

Take a random sample of 50 of the 160 benchmark questions, have two independent domain experts with full database documentation identify the columns needed to measure each question, and measure their agreement with the benchmark labels; if the experts agree with the labels on fewer than about 60 percent of columns, or if the experts disagree with one another as much as they disagree with the labels, the ground truth is not stable enough to support the paper's conclusions. A second check: if a system given the source paper text were to retrieve the target columns at near-perfect recall, the difficulty would lie in reconstructing measurements from text rather than in discovering them from the question.

Watch

Extended reading notes

Core claim

The paper's central claim is that OADD is a distinct, previously unstudied discovery problem: given a broad research question and a database, the correct answer is a set of columns that jointly realize a scientifically defensible measurement, even when no single column resembles the question. To make this concrete, the paper constructs OADD-Bench by treating empirical papers as records of schema in use: a question miner extracts and reframes paper-supported questions, a paper-conditioned column miner reconstructs the measurements and grounds them to exact database identifiers, and only mappings supported by the publication and database documentation are admitted. The resulting evaluation shows that all tested methods fall short: the strongest OADD-directed agent, with an output budget five times the target size, covers only 46.5 percent of the target columns, while direct retrieval covers at most 18.5 percent. The paper concludes that OADD is an open problem requiring methods that reason about measurement bundles rather than direct relevance.

Load-bearing premise

The load-bearing premise is that the benchmark labels correctly capture how the published papers actually measured their concepts, since the recall numbers compare systems against those labels; if the LLM-produced labels are biased or incomplete, the reported gap may overstate the difficulty of OADD.

Editorial extensions

If this is right

  • OADD-Bench gives the data management community a reproducible target: systems must recover indirect measurement bundles, not just directly relevant columns.
  • Schema-linking mechanisms that optimize direct query–column relevance transfer poorly to OADD, so new methods need to model joint realizability of a measurement.
  • The performance gap is not an output-budget artifact: increasing the allowed outputs from twice to five times the target size barely changes recall, while the best agent's average output stays far below its allowance.
  • Within a fixed agent design, model quality strongly affects bundle discovery, with recall at five-times budget rising from 0.272 to 0.465 across the tested models.
  • The benchmark opens the way for interactive researcher validation, provenance-aware grounding, and discovery across repeated survey waves.

Reading between the lines

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

  • If the operationalization framing is right, data discovery in scientific repositories should move from ranking individual columns to proposing measurement bundles with explanations, and evaluation should reward covering the components of a defensible measure.
  • A natural test of the framing's generality is to apply the same paper-grounded construction method to other longitudinal repositories; if the gap is much smaller there, the difficulty may be specific to the benchmark rather than intrinsic to operationalization.
  • Because 140 of the 160 label sets were produced by an LLM miner without human gold verification, a large human-gold annotation study would calibrate whether the reported recall numbers measure true discoverability or agreement with LLM reconstructions.
  • The grounding-or-abstain mechanism suggests that a deployable OADD system should be allowed to report that a database cannot support a question, rather than being forced to output a ranking.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces operationalization-aware data discovery (OADD), a data-discovery task in which a broad research question must be mapped to concrete database columns that jointly form a scientifically defensible measure. It constructs OADD-Bench from Health and Retirement Study (HRS) papers by using an LLM pipeline to extract questions and reconstruct paper-reported measurements, grounding them to HRS identifiers. The paper evaluates lexical and dense retrieval, five schema-linking adaptations, and LLM-based agents under a question-only, paper-hidden protocol, reporting exact-column recall at R, 2R, and 5R. The central claim is that direct retrieval reaches at most 0.185 recall, the strongest schema-linking adaptation reaches 0.401, and the best OADD-directed agent reaches 0.465, showing that OADD remains an open problem.

Significance. The problem is real and well motivated; the motivating example and the formal definition in Section 2.1 are clear. The benchmark construction treats published papers as external evidence, and the release includes provenance JSON, scoring code, and HRS metadata, which are concrete strengths. The evaluation boundary is carefully documented: systems receive only questions, permitted years, and metadata, while source papers are hidden. If the benchmark labels and the single-target recall metric are accepted, the results would provide a valuable diagnostic showing that existing retrieval and schema-linking techniques are insufficient for operationalization-driven discovery. The main risks are the validity of the LLM-generated labels and the interpretation of exact-column recall against one paper-specific target; the paper is honest about some limitations but does not resolve them.

major comments (3)
  1. [Section 3.4] The benchmark labels are load-bearing, yet 140 of 160 examples are produced by the GPT-5.5 column miner with deterministic checks but no human gold verification; for the remaining 20, human annotations are explicitly not treated as gold. The reported macro set F1 of 0.933 on 640 labels implies roughly 7% disagreement between two reconstructions even on the checked subset. Since every recall number in Table 2 is computed against these labels, a systematic LLM bias in reconstructing measurements would directly masquerade as system failure. Please provide either a human gold verification on a larger sample, an error analysis comparing LLM-reconstructed mappings to the source papers, or a sensitivity analysis that rescore the benchmark with a verified subset to demonstrate that the recall gap is not an artifact of label generation.
  2. [Section 2.3] Recall@R, Recall@2R, and Recall@5R are computed against the single column set used in the source publication, but the problem definition in Section 2.1 asks for 'a scientifically defensible operationalization,' and Section 2.2 explicitly acknowledges that other defensible measurements may exist. A system that returns a different but defensible bundle scores zero if it does not overlap the paper's set, and no precision or alternative-validity evaluation is reported in Table 2. Thus low recall does not by itself establish the conclusion that OADD remains open: the evaluation conflates 'missing the paper's particular operationalization' with 'failing to operationalize the concept.' This concern is independent of label quality. Please add precision or alternative-validity analyses, such as expert judgments on randomly sampled outputs, or an evaluation that credits alternative defensible operationalizations.
  3. [Section 3.3] The same model family (GPT-5.5) is used both by the column miner that generates the benchmark labels and by the best-performing agent in Table 2. Although the construction pipeline uses paper-derived evidence and deterministic checks, shared priors between label generator and evaluator could inflate the OADD agent's recall relative to methods not built on the same model. I would like to see either an agent evaluation with a different model family or an analysis showing that benchmark labels are stable when the column miner is run with a different backbone. Without this, the GPT-5.5 agent's 0.465 recall should be interpreted cautiously.
minor comments (5)
  1. [Section 3.4] Please clarify how the 20 annotated examples were selected and whether they overlap the 140 miner-produced examples or are additional examples; the current text says only that they come from distinct papers.
  2. [Table 1] Identifier formatting is inconsistent, e.g., 'OLB028B3' versus 'OLB028B01'; a uniform zero-padded scheme would avoid ambiguity.
  3. [Section 2.3] The sentence 'Across many questions, low recall at 5R means repeatedly missing fields already demonstrated to support the focal concepts' should be qualified, since low recall can also mean finding a different defensible operationalization rather than failing the task.
  4. [Section 4.2] Only the AutoLink-versus-agent difference is reported with a bootstrap confidence interval; the other headline comparisons in Table 2 are point estimates without uncertainty. Reporting CIs or variance estimates for the main comparisons would strengthen the conclusions.
  5. [Section 3.3] To support reproducibility, please include the exact prompt templates used for the question miner and column miner in the artifact or an appendix.

Circularity Check

1 steps flagged · score 4.0 of 10

GPT-5.5 both generates the OADD-Bench labels and powers the best-scoring agent, so the headline recall gap is partly a self-agreement score; publication grounding and LLM-independent baselines keep this moderate, not total.

  1. other [Section 3.3 (Column miner), Section 3.4 (Evidence-based selection and scale), Section 4.1 (OADD agents), Table 2.]
    "All LLM passes use GPT-5.5 with schema-constrained outputs. ... Human annotations are not treated as gold: this reconstruction itself requires scientific and HRS expertise. Agreement therefore checks consistency between two reconstructions; ... We run this design with GPT-5.4 nano, GPT-5.4 mini, and GPT-5.5."

    The 140 machine-labeled benchmark targets are produced by the GPT-5.5 column miner, and the only human check is explicitly not treated as gold. The best performing system in Table 2 is that same GPT-5.5 model acting as an OADD agent. Its Recall@5R of 0.465 therefore measures how well one GPT-5.5 pass reproduces column sets selected by another GPT-5.5 pass over the same HRS metadata, not an independent measurement of publication-grounded discoverability. The shared prior can inflate the reported recall without any external gold confirmation, so the conclusion that even the strongest agent misses more than half of the targets is not independently validated.

full rationale

The benchmark is substantially anchored outside the fitted loop: target labels are reconstructed from peer-reviewed HRS studies and official HRS documentation, and the direct-retrieval baselines (BM25, TF-IDF, BGE, SPLADE, fusion) do not use GPT-5.5 at all, so their low recall is not a self-agreement artifact. However, the headline OADD-agent result is circular in a moderate sense, because GPT-5.5 is both the label generator and the best evaluator, and Section 3.4 explicitly declines to treat human annotations as gold. The alternative-operationalization limitation (exact-column recall against one paper's measurement) is a benchmark-validity concern rather than a circular reduction, so it does not further raise the score. Overall, the central research question is new and externally motivated, but the strongest performance claim should be read as demonstrating self-consistency of GPT-5.5 rather than independently verified success.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the validity of LLM-generated labels, the equation of publication-documented use with scientific defensibility, and design choices about benchmark composition. There are no fitted numerical constants; the two free parameters are evaluation and selection thresholds chosen by hand.

free parameters (2)
  • Minimum number of grounded columns per benchmark example = 2
    Admission rule in Section 3.4; excludes single-column operationalizations and forces the benchmark toward bundle discovery, shaping all recall numbers.
  • Output budget multipliers for evaluation = R, 2R, 5R
    Evaluation budgets defined in Section 2.3; all headline recall figures are reported at these hand-chosen multipliers of target size.
assumptions (4)
  • domain assumption A published paper's documented use of HRS fields makes those fields a scientifically defensible operationalization of the focal concept.
    Section 2.2: 'Documented use makes V a strong positive target.' The benchmark's gold labels depend on this equivalence.
  • ad hoc to paper The LLM column miner (GPT-5.5) correctly reconstructs paper measurements and grounds them to HRS identifiers whenever deterministic checks pass.
    Section 3.3-3.4: 140 of 160 examples rely on the miner with no human gold; the 20-example consistency check treats human annotations as not gold.
  • domain assumption HRS codebook documentation is complete and authoritative for the universe of eligible columns.
    Section 3.3 grounding step loads official HRS metadata and rejects identifiers absent from evidence; if docs are incomplete, labels can be wrong.
  • domain assumption Eligible papers acquired through open-access and a 500-record sample are representative of HRS analytic practice.
    Section 3.4 describes selection; the release is explicitly 'not a sample of all HRS studies', so generalization of recall numbers beyond these questions is limited.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From Research Questions to Columns: Operationalization-Aware Data Discovery." pith.science (2026). https://pith.science/paper/S52E3TUQ

@misc{pith2026260804536,
  author       = {Pith},
  title        = {Pith review of: From Research Questions to Columns: Operationalization-Aware Data Discovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S52E3TUQ}},
  note         = {Machine review of arXiv:2608.04536}
}
read the original abstract

Researchers often approach a data repository with an abstract concept and ask which columns can measure it. Useful columns may not resemble the query; they may matter only as complementary indicators in a defensible measure. This need differs from schema linking and column retrieval, which begin from more explicit needs and reward direct relevance. We define operationalization-aware data discovery (OADD): given a broad question and a database, optionally under a scope constraint, OADD jointly determines how focal concepts can be measured with available data and identifies supporting columns. Developing OADD methods requires examples for design and evaluation, but asking researchers to supply conceptual questions and their columns is impractical. We construct OADD-Bench by treating empirical papers as records of schema in use. A question miner extracts and reframes a paper-supported question; a paper-conditioned column miner reconstructs its measurements and grounds them to database identifiers. We admit only mappings supported by the publication and database documentation. OADD-Bench contains 160 questions from 111 papers and 4,682 question-column labels. Each target records a measurement used in published research; the paper supplies the precedent, while the miners extract and ground it. We evaluate lexical and neural retrieval, adapted schema-linking systems, and large language model (LLM) OADD agents. Each method receives only a question, permitted years, and dataset metadata; source papers are used only to construct and document benchmark labels. At the largest output limit, direct retrieval reaches at most 0.185 recall. The strongest schema-linking adaptation reaches 0.401 but remains optimized for a different objective; an OADD-directed agent performs best at 0.465. Even this agent covers less than half the target columns, showing that OADD remains an open problem.

Figures

Figures reproduced from arXiv: 2608.04536 by the authors.

Figure 1
Figure 1. Operationalization connects a concept to available [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Papers provide construction evidence; evaluated [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 21 canonical work pages

  1. [1]

    Muhammad Imam Luthfi Balaka, David Alexander, Qiming Wang, Yue Gong, Adila Krisnadhi, and Raul Castro Fernandez. 2025. Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System.Proceedings of the ACM on Management of Data3, 3, Article 200 (2025), 28 pages. doi:10.1145/ 3725337

    work page 2025

  2. [2]

    Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel Weld. 2020. TLDR: Extreme Summarization of Scientific Documents. InFindings of the Association for Com- putational Linguistics: EMNLP 2020. Association for Computational Linguistics, 4766–4777. doi:10.18653/v1/2020.findings-emnlp.428

    doi:10.18653/v1/2020.findings-emnlp.428 2020

  3. [3]

    Torres, Yulin Yang, Karla Renata Flores Romero, Mary C

    Irena Cenzer, Jacqueline M. Torres, Yulin Yang, Karla Renata Flores Romero, Mary C. Thoma, and Ashwin A. Kotwal. 2025. Development and Evaluation of a Brief Social Isolation Measure in the Nationally-Representative Health and Retirement Study.Journal of the American Geriatrics Society73, 12 (2025), 3807–

    work page 2025

  4. [4]

    Cormack, Charles L

    Gordon V. Cormack, Charles L. A. Clarke, and Stefan Buettcher. 2009. Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods. InProceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 758–759. doi:10.1145/1571941.1572114

    arXiv 2009

  5. [5]

    Nadine Dulisch, Andreas Oskar Kempf, and Philipp Schaer. 2015. Query Expansion for Survey Question Retrieval in the Social Sciences. InResearch and Advanced Technology for Digital Libraries: TPDL 2015. Springer, 28–39. doi:10.1007/978-3-319-24592-8_3

    work page doi:10.1007/978-3-319-24592-8_3 2015

  6. [6]

    Grace Fan, Jin Wang, Yuliang Li, Dan Zhang, and Renée J. Miller. 2023. Semantics- aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning.Proceedings of the VLDB Endowment16, 7 (2023)

    work page 2023

  7. [7]

    Raul Castro Fernandez, Ziawasch Abedjan, Samuel Madden, and Michael Stone- braker. 2018. Aurum: A Data Discovery System. In2018 IEEE 34th International Conference on Data Engineering. 1001–1012. doi:10.1109/ICDE.2018.00094

    arXiv 2018

  8. [8]

    Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant

Show all 37 references
  1. [9]

    Mark Green, Maura Halstead, Caroline Jay, Richard Kingston, Alex Singleton, and David Topping. 2026. Comparing How Large Language Models Perform against Keyword-Based Searches for Social Science Research Data Discovery. arXiv preprint arXiv:2601.19559(2026). doi:10.48550/arXiv...

    2026 doi

  2. [10]

    Health and Retirement Study. 2026. HRS Bibliography. Accessed June 2026. https://hrs.isr.umich.edu/publications/biblio/

    2026

  3. [11]

    Mayank Kothyari, Dhruva Dhingra, Sunita Sarawagi, and Soumen Chakrabarti

  4. [12]

    Kranz, Jemar R

    Emiko O. Kranz, Jemar R. Bather, Xiaoyan Zhang, Virginia W. Chang, Steven W. Cole, and Adolfo G. Cuevas. 2026. Discrimination Exposure and Lymphocyte Differentiation: Results from the Health and Retirement Study.Brain, Behavior, & Immunity - Health52 (2026), 101170. doi:10.101...

    2026

  5. [13]

    Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023. RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 13067–13075. doi:10.1609/aaai.v37i11. 26535

    2023 doi

  6. [14]

    Xiao Liu, Hao Yu, Hanchen Zhang, et al. 2024. AgentBench: Evaluating LLMs as Agents. InInternational Conference on Learning Representations

    2024

  7. [15]

    Jalal Mahmud and Eser Kandogan. 2026. Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows.arXiv preprint arXiv:2607.00828(2026). doi:10.48550/arXiv.2607.00828

    2026 doi arXiv

  8. [16]

    Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Bhavana Dalvi Mishra, Abhijeetsingh Meena, Aryan Prakhar, Tirth Vora, Tushar Khot, Ashish Sabharwal, and Peter Clark. 2025. DiscoveryBench: Towards Data-Driven Discovery with Large Language Models. InInternational Con...

    2025

  9. [17]

    Md Mahadi Hasan Nahid, Davood Rafiei, Weiwei Zhang, and Yong Zhang. 2026. Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL. InFindings of the Association for Computational Linguistics: EACL 2026. Association for Computational Linguist...

    2026

  10. [18]

    OpenAI. 2023. GPT-4 Technical Report.arXiv preprint arXiv:2303.08774(2023). doi:10.48550/arXiv.2303.08774

    2023 doi arXiv

  11. [19]

    Stephen Robertson and Hugo Zaragoza. 2009. The Probabilistic Relevance Frame- work: BM25 and Beyond.Foundations and Trends in Information Retrieval3, 4 (2009), 333–389. doi:10.1561/1500000019

    2009 doi

  12. [20]

    Smith, and Ashwin A

    Mara Rosenberg, Irena Cenzer, Alexander K. Smith, and Ashwin A. Kotwal. 2026. End-of-Life Loneliness, Social Isolation, and Symptom Burden: A Nationally- Representative Study.Journal of the American Geriatrics Society(2026). doi:10. 1111/jgs.70398

    2026

  13. [21]

    Gerard Salton and Christopher Buckley. 1988. Term-Weighting Approaches in Automatic Text Retrieval.Information Processing & Management24, 5 (1988), 513–523. doi:10.1016/0306-4573(88)90021-0

    1988 doi

  14. [22]

    Faul, Mary Beth Ofstedal, Kenneth M

    Amanda Sonnega, Jessica D. Faul, Mary Beth Ofstedal, Kenneth M. Langa, John W. R. Phillips, and David R. Weir. 2014. Cohort Profile: the Health and Retirement Study (HRS).International Journal of Epidemiology43, 2 (2014), 576–585. doi:10. 1093/ije/dyu067

    2014

  15. [23]

    Chen Su, Sen Zhang, Qiandan Zheng, Jie Miao, and Junhong Guo. 2025. Self- Perceptions of Aging and Sarcopenia in Older Adults: The Mediating Role of IADL.Frontiers in Medicine12 (2025), 1693158. doi:10.3389/fmed.2025.1693158

    2025

  16. [24]

    Sina Taslimi, Artemis Capari, Hosein Azarbonyad, Zi Long Zhu, Zubair Afzal, Evangelos Kanoulas, and George Tsatsaronis. 2025. Extracting, Detecting, and Generating Research Questions for Scientific Articles. InProceedings of the 31st International Conference on Computational L...

    2025

  17. [25]

    Tornike Tsereteli, Yavuz Selim Kartal, Simone Paolo Ponzetto, Andrea Zielinski, Kai Eckert, and Philipp Mayr. 2022. Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications. InProceedings of the Third Workshop on Scholarly Docu...

    2022 doi

  18. [26]

    Tornike Tsereteli, Daniel Ruffinelli, and Simone Paolo Ponzetto. 2024. Enriching Social Science Research via Survey Item Linking.arXiv preprint arXiv:2412.15831 (2024). doi:10.48550/arXiv.2412.15831

    2024 doi arXiv

  19. [27]

    Słomczyński, Spyros Blanas, and J

    Yamei Tu, Olga Li, Junpeng Wang, Han-Wei Shen, Przemek Powałko, Irina Tomescu-Dubrow, Kazimierz M. Słomczyński, Spyros Blanas, and J. Craig Jenkins

  20. [28]

    Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, Tongshuang Wu, and Graham Neubig. 2023. DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions. InProceedings of ACL. 10288–10303. doi:10. 18653/v1/2023.acl-long.573

    2023

  21. [29]

    Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020. RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6778–6788. doi:10.1...

    2020 doi

  22. [30]

    Yihan Wang, Peiyu Liu, and Xin Yang. 2025. LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 977–991. doi:10.18653/v1/2025.emnlp-main.51

    2025 doi

  23. [31]

    doi:10.1109/TVCG.2023.3261944

    SDRQuerier: A Visual Querying Framework for Cross-National Survey Data Recycling.IEEE Transactions on Visualization and Computer Graphics29, 6 (2023), 2862–2874. doi:10.1109/TVCG.2023.3261944

    2023

  24. [32]

    Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. 2024. C-Pack: Packed Resources for General Chinese Embeddings. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association...

    2024

  25. [33]

    Yi Yang, Hanyu Duan, Jiaxin Liu, and Kar Yan Tam. 2024. LLM-Measure: Generat- ing Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research.arXiv preprint arXiv:2409.12722(2024). doi:10.48550/arXiv.2409.12722

    2024 doi arXiv

  26. [35]

    Ziyang Wang, Yuanlei Zheng, Zhenbiao Cao, Xiaojin Zhang, Zhongyu Wei, Pei Fu, Zhenbo Luo, Wei Chen, and Xiang Bai. 2026. AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale. InProceedings of the AAAI Conference on Artificia...

    2026 doi

  27. [2022]

    InProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

    From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective. InProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2353–2359. doi:10.1145/3477495.3531857

  28. [2023]

    InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

    CRUSH4SQL: Collective Retrieval Using Schema Hallucination for Text2SQL. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 14054–14066. doi:10.18653/v1/2023.emnlp-main.868

    2023 doi

  29. [3813]

    doi:10.1111/jgs.70056

    doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.