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REVIEW 4 major objections 4 minor 39 references

A Design Space for the Critical Validation of LLM-Generated Tabular Data

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Two dimensions organize every way to validate LLM-generated tabular data, the paper argues, by pairing data source with analysis granularity.

desk verdict A sensible, honestly-limited design space for a young subfield; the circularity is real but disclosed, and the paper's value is in giving the community a shared vocabulary. read the letter →

arxiv 2505.04487 v1 pith:2OE2NYCX submitted 2025-05-07 cs.HC

Madhav Sachdeva , Christopher Narayanan , Marvin Wiedenkeller , Jana Sedlakova , Jürgen Bernard This is my paper · ORCID
classification cs.HC
keywords designspaceLLM-generatedtabulardatacriticalvalidationvisualanalyticsanalysisgranularitygroundtruthcomparisonLLMexplanations
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 tries to establish that the fast-growing but unstructured field of validating LLM-generated tabular data can be systematically organized by a two-dimensional design space. The first dimension is the Data Source used for validation: the LLM-generated values themselves, ground truth values, LLM-generated explanations, or combinations of these. The second dimension is Analysis Granularity, from inspecting a single item, to aggregating many items, to relating attributes at 1x1, 1xm, and mxm scope. The authors demonstrate the space by mapping 19 existing validation approaches onto it, specifying the dominant validation task in each cell, and walking through two tools in detail. A sympathetic reader would care because this gives researchers and tool builders a common language for comparing approaches, choosing a validation strategy, and spotting empty regions worth exploring.

What carries the argument

The central object is the design space itself, a two-dimensional grid whose axes are Data Source (LLM-generated values, ground truth, LLM explanations, and combinations) and Item/Attribute Granularity (1 item, m items, 1x1, 1xm, mxm). It functions as a classification lattice: each cell names a validation task with its expected outcome and a representative question, so any existing or proposed approach can be located by what data it compares and at what analysis scope. The mapping of 19 approaches onto the grid is the mechanism that demonstrates the space's descriptive power and makes gaps visible.

What would settle it

A published validation tool whose defining feature is, for example, its level of automation or its place in the generation-to-application workflow, but that falls into the same (Data Source, Granularity) cell as an existing tool, would show the two dimensions are not expressive; surveying a broader set of validation papers and checking whether any essential distinction fails to change the cell is a direct test.

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Extended reading notes

Core claim

The central claim is that the cross-product of two discrete dimensions—Data Source and Item/Attribute Granularity—forms a design space that can describe every critical validation approach for LLM-generated tabular data. Data Source distinguishes LLM-generated values (V), ground truth values (Vg), and LLM-generated explanations (E), including pairwise and triple combinations; Analysis Granularity distinguishes atomic single-item validation, multi-item validation within one attribute, and across-attribute validation at three scopes (1x1, 1xm, mxm). For each of the resulting cells the authors define the dominant validation task, its expected analysis outcome, and an illustrative question, such as comparing LLM and ground truth value distributions at the m-items level to reveal bias, or comparing relation overviews at the mxm level to see whether LLM-generated attributes reproduce known dependencies. The paper shows the space has descriptive power by mapping 19 existing approaches onto it and describing two in detail, iScore and LLM Comparator, including the sequences of cells their workflows traverse. The authors further identify that the lower-right region—explanations aggregated across multiple attributes—is so far uncharted.

Load-bearing premise

The whole framework rests on the assumption that two dimensions—what data is compared and at what granularity—are enough to capture the meaningful differences between validation approaches, an assertion the paper accepts without an empirical or theoretical proof.

Editorial extensions

If this is right

  • Tool developers can design new validation workflows by selecting an empty or sparsely populated cell, such as relating LLM explanations across multiple attributes.
  • Researchers can compare validation methods by their grid position rather than by informal labels, making commonalities and differences explicit.
  • The space clarifies that value-based validation is measurable and statistical, whereas explanation-based validation requires subjective plausibility judgments, a distinction that shapes visualization choices.
  • The observed density of the grid suggests that comparisons of LLM values against ground truth are the dominant validation strategy, while explanation-only approaches are rare.
  • The uncharted lower-right region indicates a concrete research opportunity in aggregating explanations at attribute level.

Reading between the lines

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

  • A third dimension capturing degree of human involvement or workflow phase (generation, validation, downstream application) could be added; the authors raise this possibility, and a test of the two dimensions' sufficiency is whether such additions preserve the current mapping.
  • The grid could double as an audit checklist for LLM data pipelines: mapping each validation step to a cell exposes which comparisons are never performed, which is itself a risk signal.
  • Because the paper's illustrations are numerical, an immediate extension is to populate the same cells with categorical-data idioms, which would test whether the design space stays stable across attribute types.
  • The low density of explanation-only cells suggests that the community currently validates outputs more than reasoning; if explanations matter for trust, those cells are where new interactive tools would have the largest effect.
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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 / 4 minor

Summary. The paper proposes a two-dimensional design space for the critical validation of LLM-generated tabular data. The Data Source dimension distinguishes LLM-generated values (V), ground truth values (Vg), LLM explanations (E), and their combinations; the Item and Attribute Granularity dimension runs from 1-item validation through m-item and 1x1, 1xm, and mxm attribute-level analyses. For each cross-cut the authors define a dominant validation task and example analysis questions. They map 19 existing approaches onto the space in Table 1, walk through iScore and LLM Comparator in detail, and discuss uncharted cells and limitations. The central claim is that the design space systematically structures the validation literature, demonstrates descriptive power through the mapping, and supports gap identification.

Significance. The design space is simple, communicable, and plausibly useful for visual analytics researchers seeking to understand or build validation tools. The detailed walkthroughs of iScore and LLM Comparator are instructive, and the authors are transparent about the coarse-grained mapping choices. The main value is the consolidated overview of a fragmented and emerging literature. However, significance depends on whether the claimed descriptive power and gap analysis can be established independently of the construction process; the current evidence is partly circular and incomplete because the referenced supplemental characterization is absent. With a reproducible mapping protocol and a validation procedure, the paper could make a useful contribution to the field.

major comments (4)
  1. [Section 2.1, Table 1] The central claim of descriptive power is demonstrated on the same 19 works that served as 'the basis for design space ideation' (Section 2.1). Because the dimension definitions, the cell tasks, and the mapping conventions were abstracted from these papers, a good fit is expected by construction; the mapping in Table 1 is therefore partly a re-description rather than an independent test. This is load-bearing for Contributions 1 and 2. Please validate the space on a held-out set of works not used during ideation, report an inter-rater agreement study for the mapping, or explicitly reframe the contribution as an illustrative mapping rather than a demonstration of descriptive power.
  2. [Section 4] The empty cells and 'uncharted' regions are partly artifacts of the mapping conventions. The Data Source Mapping paragraph maps approaches that use two data sources only to the combined cell, and the Item/Attribute Granularity Mapping paragraph maps multi-granularity approaches only to the coarser cell. Under the alternative convention of mapping every applicable cell, several explanation-based attribute-level cells could be populated; for example, iScore's attention visualization is described in Section 3 as a non-textual explanation and is mapped under V:E, even though Section 2.2 defines E as textual justifications. Please report the mapping under a multi-cell convention or a sensitivity analysis, and distinguish artifacts from genuine research gaps when discussing the lower-right 'uncharted' region.
  3. [Section 2.1] The literature search is not fully reproducible. It relies on Google Scholar queries, forward/backward search seeded from four papers, and subjective exclusion criteria; the referenced 'supplemental material' with the characterization of the 19 works is not available in the arXiv version, so readers cannot audit the coding that underlies Table 1. Please provide the full search protocol, inclusion/exclusion decisions, and a per-work table of extracted features (data sources, granularities, validation tasks, visualization idioms, workflow phase) as an appendix so that the mapping can be verified and extended.
  4. [Section 2.1, Section 4] The assertion that the two dimensions are 'expressive, independent dimensions' is not justified. The paper does not show that all relevant aspects of validation approaches—degree of automation, workflow phase, data type—are either captured by or orthogonal to the chosen two dimensions; Section 4 itself lists workflow phases, human-in-the-loop patterns, and categorical-versus-numeric data as additional structural characteristics. Please provide a conceptual argument or empirical analysis for the chosen two dimensions, or soften the claim that the design space 'systematically structures' the validation landscape to a more modest scoping claim.
minor comments (4)
  1. [Abstract, Section 2.3] The Abstract and Section 2.3 use different names for the granularity dimension ('Analysis Granularity' versus 'Item and Attribute Granularity'); align the terminology throughout.
  2. [Section 2.1, Section 3] Section 2.1 excludes works 'addressing only textual data' yet includes LLM Comparator, which is described in Section 3 as primarily focused on LLM-generated text; clarify why it satisfies the tabular-data scope.
  3. [Figures 2 and 3] The numbered steps and arrows in Figures 2 and 3 are not fully explained in a figure caption; add a caption or text reference so that the workflow mapping is readable without access to the original tools.
  4. [Section 2.4] The relation between the cell descriptions and the example analysis questions is informal; consider labeling each task with a stable identifier (e.g., T1..T30) to enable unambiguous reference in Table 1 and future extensions.

Circularity Check

2 steps flagged · score 6.0 of 10

Design-space validation is circular: the 19 mapped works are the same corpus from which the space was ideated, and the reported gaps are partly produced by mapping conventions.

  1. fitted input called prediction [Section 2.1 (Methodology and Overview); Contribution 2 (Introduction)]
    "In total, we identified 19 relevant works as the basis for design space ideation; we refer to the supplemental material for their characterization. ... A mapping of existing validation approaches onto this design space, demonstrating its descriptive power."

    The design space's dimensions, cell definitions, and task descriptions were abstracted from the same 19 papers that are then mapped back in Table 1 and used to claim descriptive power. Mapping a corpus onto categories derived from that corpus is expected to fit; it cannot independently demonstrate descriptive power or reveal gaps. The demonstration is therefore a re-description of the input corpus, not an external validation.

  2. self definitional [Section 4, Limitations and Discussion (Data Source Mapping; Item/Attribute Granularity Mapping)]
    "When validation approaches use two Data Source Dimension elements (values, ground truth, explanations), we currently map them only to the combined cell rather than separately, leading to empty explanation-only cells never occurring in isolation. ... Our mapping prioritizes the coarser cell to reduce redundancy, but this may impact the descriptiveness of the design space, requiring further investigation."

    The empty 'uncharted' cells (e.g., across-attribute explanation validation) are not purely empirical findings; they follow from the disclosed mapping convention that every multi-source approach is placed only in the combined cell and every multi-granularity approach only in the coarser cell. Under the alternative convention of mapping all applicable cells, the same underlying works could populate some of those empty cells. Thus the claimed gap identification is partly an artifact of the mapping rules, i.e., defined by the method rather than discovered from the data.

full rationale

The circularity is real but partial. The design space categories (Data Source and Item/Attribute Granularity) are conceptually plausible and could in principle be applied to new approaches, so the framework is not simply a tautology. However, the paper's main evidence for its central claim—demonstrating descriptive power—consists of mapping the same 19 works that were 'the basis for design space ideation' back onto the space (Table 1). This is a train-on-the-test-set pattern: the taxonomy was abstracted from those papers, so a good fit is guaranteed and cannot independently validate the framework. Likewise, the identification of gaps ('Blank cells at the lower right') is not fully empirical: the paper's explicit mapping conventions place any multi-source approach only in the combined cell and any multi-granularity approach only in the coarser cell, so the empty cells are partly created by the method's own mapping rules. The absence of the promised supplemental characterization of the 19 works further prevents an external audit of the mapping. No load-bearing self-citation chain or imported uniqueness theorem is present; the authors' own prior works [BSW14, SBKB23] are used only as illustrative citations, not as evidence for the framework. Overall, the derivation does not reduce entirely to its inputs—the two dimensions have independent content—but the demonstration of descriptive power is partly circular, so a score of 6 is appropriate.

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

The design space rests on domain assumptions about the expressiveness of the two dimensions, the representativeness of the 19 papers, and the feasibility of discrete mapping; there are no fitted parameters or invented entities.

assumptions (3)
  • domain assumption The Data Source and Item/Attribute Granularity dimensions are expressive and independent enough to structure the validation space.
    Centered design choice in Section 2.1; no empirical or analytical justification beyond the authors' synthesis of 19 works.
  • domain assumption The 19 identified works are a representative sample of validation approaches for LLM-generated tabular data.
    Search was conducted via Google Scholar with five terms and forward/backward search; selection criteria are minimal and no completeness guarantee is provided (Section 2.1).
  • domain assumption Approaches can be unambiguously assigned to discrete cells of the design space.
    The mapping in Table 1 requires discretization; the paper itself notes prioritization of coarser cells and combined data sources, implying ambiguity (Section 4).

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Pith. "Pith review of A Design Space for the Critical Validation of LLM-Generated Tabular Data." pith.science (2026). https://pith.science/paper/2OE2NYCX

@misc{pith2026250504487,
  author       = {Pith},
  title        = {Pith review of: A Design Space for the Critical Validation of LLM-Generated Tabular Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OE2NYCX}},
  note         = {Machine review of arXiv:2505.04487}
}
read the original abstract

LLM-generated tabular data is creating new opportunities for data-driven applications in academia, business, and society. To leverage benefits like missing value imputation, labeling, and enrichment with context-aware attributes, LLM-generated data needs a critical validation process. The number of pioneering approaches is increasing fast, opening a promising validation space that, so far, remains unstructured. We present a design space for the critical validation of LLM-generated tabular data with two dimensions: First, the Analysis Granularity dimension: from within-attribute (single-item and multi-item) to across-attribute perspectives (1 x 1, 1 x m, and n x n). Second, the Data Source dimension: differentiating between LLM-generated values, ground truth values, explanations, and their combinations. We discuss analysis tasks for each dimension cross-cut, map 19 existing validation approaches, and discuss the characteristics of two approaches in detail, demonstrating descriptive power.

Figures

Figures reproduced from arXiv: 2505.04487 by the authors.

Figure 1
Figure 1. Design space for the critical validation of LLM-generated tabular data. The x-dimension describes the analysis granularity, rang￾ing from within attributes to across attributes. The y-dimension structures approaches by the data used for validation, including generated values, explanations, ground truth data, and their combinations. Visual representations per cell exemplify numerical data use, while the design space … view at source ↗
Figure 2
Figure 2. Mapping the supported tasks and user workflows of two existing validation approaches to our design space. Purple: iScore [CHM∗ 24], Yellow: LLM Comparator [KTP∗ 24] [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Screenshot with marked validation steps of iScore re￾produced from Coscia et al. [CHM∗ 24] with permission (left) and LLM Comparator [KTP∗ 24] with permission (right) iScore [CHM∗ 24] is an LLM-based tool for scoring summaries on attributes, such as objectivity and coherence. A scatter plot displays ground truth data for two scoring attributes, providing an overview of expected trends and correlations (1). A bar cha… view at source ↗

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Works this paper leans on

39 extracted references · 19 canonical work pages

  1. [1]

    : Self-rag: Learning to retrieve, generate, and critique through self-reflection

    Asai A., Wu Z., Wang Y., Sil A., Hajishirzi H. : Self-rag: Learning to retrieve, generate, and critique through self-reflection. In Conference on Learning Representations (2023)

    work page 2023

  2. [2]

    : Cycles of thought: Measuring llm confidence through stable explanations

    Becker E., Soatto S. : Cycles of thought: Measuring llm confidence through stable explanations. arXiv:2406.03441 (2024)

    arXiv 2024

  3. [3]

    : Language models are realistic tabular data generators

    Borisov V., Se ler K., Leemann T., Pawelczyk M., Kasneci G. : Language models are realistic tabular data generators. arXiv preprint arXiv:2210.06280 (2022)

    arXiv 2022

  4. [4]

    M., Scharl A., Nixon L

    Brasoveanu A. M., Scharl A., Nixon L. J., Andonie R. : Visualizing large language models: A brief survey. In Conference Information Visualisation (IV) (2024), IEEE, pp. 236--245

    work page 2024

  5. [5]

    : Visual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Sets

    Bernard J., Steiger M., Widmer S., L \" u cke-Tieke H., May T., Kohlhammer J. : Visual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Sets . Computer Graphics Forum (CGF) 33, 3 (2014), 291--300

    work page 2014

  6. [6]

    : Knowledgevis: Interpreting language models by comparing fill-in-the-blank prompts

    Coscia A., Endert A. : Knowledgevis: Interpreting language models by comparing fill-in-the-blank prompts. IEEE Transactions on Visualization and Computer Graphics 30, 9 (2023), 6520--6532

    work page 2023

  7. [7]

    A., Fu E., Bertucci D., Holstein K., Talwalkar A., Hong J

    Cabrera \'A . A., Fu E., Bertucci D., Holstein K., Talwalkar A., Hong J. I., Perer A. : Zeno: An interactive framework for behavioral evaluation of machine learning. In CHI Conference on Human Factors in Computing Systems (2023), pp. 1--14

    work page 2023

  8. [8]

    S., Crossley S., Endert A

    Coscia A., Holmes L., Morris W., Choi J. S., Crossley S., Endert A. : iscore: Visual analytics for interpreting how language models automatically score summaries. In International Conference on Intelligent User Interfaces (2024), pp. 787--802

    work page 2024

Show all 39 references
  1. [9]

    Chiang C.-H., Lee H.-y. : Can large language models be an alternative to human evaluations? In Association for Computational Linguistics (2023), Rogers A., Boyd-Graber J., Okazaki N., (Eds.), Association for Computational Linguistics, pp. 15607--15631

    2023

  2. [10]

    Cheng F., Zouhar V., Chan R. S. M., F \"u rst D., Strobelt H., El-Assady M. : Interactive analysis of llms using meaningful counterfactuals. arXiv preprint arXiv:2405.00708 (2024)

    2024 arXiv

  3. [11]

    Fan G., Shraga R., Miller R. J. : Finding support for tabular llm outputs. VLDB Endowment. ISSN 2150 (2024), 8097

    2024

  4. [12]

    A., Zhang J., Hu Z., Qi Y., Nickleach S., Socolinsky D., Sengamedu S., Faloutsos C

    Fang X., Xu W., Tan F. A., Zhang J., Hu Z., Qi Y., Nickleach S., Socolinsky D., Sengamedu S., Faloutsos C. : Large language models (llms) on tabular data: Prediction, generation, and understanding--a survey. arXiv preprint arXiv:2402.17944 (2024)

    2024 arXiv

  5. [13]

    : Generating structured outputs from language models: Benchmark and studies

    Geng S., Cooper H., Moskal M., Jenkins S., Berman J., Ranchin N., West R., Horvitz E., Nori H. : Generating structured outputs from language models: Benchmark and studies. arXiv preprint arXiv:2501.10868 (2025)

    2025 arXiv

  6. [14]

    : Tabllm: Few-shot classification of tabular data with large language models

    Hegselmann S., Buendia A., Lang H., Agrawal M., Jiang X., Sontag D. : Tabllm: Few-shot classification of tabular data with large language models. In International Conference on Artificial Intelligence and Statistics (2023), PMLR, pp. 5549--5581

    2023

  7. [15]

    Huang S., Mamidanna S., Jangam S., Zhou Y., Gilpin L. H. : Can large language models explain themselves? a study of llm-generated self-explanations. arXiv:2310.11207 (2023)

    2023 arXiv

  8. [16]

    S., Lee Y., Shin J., Kim Y.-H., Kim J

    Kim T. S., Lee Y., Shin J., Kim Y.-H., Kim J. : Evallm: Interactive evaluation of large language model prompts on user-defined criteria. In CHI Conference (2024), pp. 1--21

    2024

  9. [17]

    X., Wexler J., Reif E., Kallarackal K., Chang M., Terry M., Dixon L

    Kahng M., Tenney I., Pushkarna M., Liu M. X., Wexler J., Reif E., Kallarackal K., Chang M., Terry M., Dixon L. : Llm comparator: Visual analytics for side-by-side evaluation of large language models. In CHI Conference on Human Factors in Computing Systems (2024), pp. 1--7

    2024

  10. [18]

    : Aligning with logic: Measuring, evaluating and improving logical consistency in large language models

    Liu Y., Guo Z., Liang T., Shareghi E., Vuli \'c I., Collier N. : Aligning with logic: Measuring, evaluating and improving logical consistency in large language models. arXiv preprint arXiv:2410.02205 (2024)

    2024 arXiv

  11. [19]

    : Prd: Peer rank and discussion improve large language model based evaluations

    Li R., Patel T., Du X. : Prd: Peer rank and discussion improve large language model based evaluations. arXiv preprint arXiv:2307.02762 (2023)

    2023 arXiv

  12. [20]

    : On llms-driven synthetic data generation, curation, and evaluation: A survey

    Long L., Wang R., Xiao R., Zhao J., Ding X., Chen G., Wang H. : On llms-driven synthetic data generation, curation, and evaluation: A survey. arXiv preprint arXiv:2406.15126 (2024)

    2024 arXiv

  13. [21]

    C., Bryan C

    Mishra A., Danzy B., Soni U., Arunkumar A., Huang J., Kwon B. C., Bryan C. : Promptaid: Visual prompt exploration, perturbation, testing and iteration for large language models. IEEE Transactions on Visualization and Computer Graphics (2025)

    2025

  14. [22]

    : A review of faithfulness metrics for hallucination assessment in large language models

    Malin B., Kalganova T., Boulgouris N. : A review of faithfulness metrics for hallucination assessment in large language models. arXiv preprint arXiv:2501.00269 (2024)

    2024

  15. [23]

    : Assessing the potentials of llms and gans as state-of-the-art tabular synthetic data generation methods

    Miletic M., Sariyar M. : Assessing the potentials of llms and gans as state-of-the-art tabular synthetic data generation methods. In Privacy in Statistical Databases (2024), Springer, pp. 374--389

    2024

  16. [24]

    : Visualization analysis and design

    Munzner T. : Visualization analysis and design. CRC press, 2014

    2014

  17. [25]

    S., Johnson J., Nair R., Daly E., Geyer W

    Pan Q., Ashktorab Z., Desmond M., Cooper M. S., Johnson J., Nair R., Daly E., Geyer W. : Human-centered design recommendations for llm-as-a-judge. arXiv:2407.03479 (2024)

    2024 arXiv

  18. [26]

    : Assessing the research landscape and clinical utility of large language models: a scoping review

    Park Y.-J., Pillai A., Deng J., Guo E., Gupta M., Paget M., Naugler C. : Assessing the research landscape and clinical utility of large language models: a scoping review. BMC Medical Informatics and Decision Making 24, 1 (2024), 72

    2024

  19. [27]

    : LFPeers : Temporal similarity search and result exploration

    Sachdeva M., Burmeister J., Kohlhammer J., Bernard J. : LFPeers : Temporal similarity search and result exploration. Computers & Graphics (2023)

    2023

  20. [28]

    L., Jaldi C

    Schroeder N. L., Jaldi C. D., Zhang S. : Large language models with human-in-the-loop validation for systematic review data extraction. arXiv preprint arXiv:2501.11840 (2025)

    2025 arXiv

  21. [29]

    G., Wu E

    Shankar S., Li H., Asawa P., Hulsebos M., Lin Y., Zamfirescu-Pereira J., Chase H., Fu-Hinthorn W., Parameswaran A. G., Wu E. : Spade: Synthesizing data quality assertions for large language model pipelines. arXiv preprint arXiv:2401.03038 (2024)

    2024 arXiv

  22. [30]

    Strobelt H., Webson A., Sanh V., Hoover B., Beyer J., Pfister H., Rush A. M. : Interactive and visual prompt engineering for ad-hoc task adaptation with large language models. IEEE trans. on visualization and computer graphics 29, 1 (2022), 1146--1156

    2022

  23. [31]

    : Comprehensive reassessment of large-scale evaluation outcomes in llms: A multifaceted statistical approach

    Sun K., Wang R., S gaard A. : Comprehensive reassessment of large-scale evaluation outcomes in llms: A multifaceted statistical approach. arXiv preprint arXiv:2403.15250 (2024)

    2024 arXiv

  24. [32]

    : Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences

    Shankar S., Zamfirescu-Pereira J., Hartmann B., Parameswaran A., Arawjo I. : Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (2024), ...

    2024

  25. [33]

    : The language interpretability tool: Extensible, interactive visualizations and analysis for nlp models

    Tenney I., Wexler J., Bastings J., Bolukbasi T., Coenen A., Gehrmann S., Jiang E., Pushkarna M., Radebaugh C., Reif E., et al. : The language interpretability tool: Extensible, interactive visualizations and analysis for nlp models. arXiv preprint arXiv:2008.05122 (2020)

    2020 arXiv

  26. [34]

    : Human-llm collaborative annotation through effective verification of llm labels

    Wang X., Kim H., Rahman S., Mitra K., Miao Z. : Human-llm collaborative annotation through effective verification of llm labels. In CHI Conference (2024), pp. 1--21

    2024

  27. [35]

    : Judging llm-as-a-judge with mt-bench and chatbot arena

    Zheng L., Chiang W.-L., Sheng Y., Zhuang S., Wu Z., Zhuang Y., Lin Z., Li Z., Li D., Xing E., et al. : Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36 (2023), 46595--46623

    2023

  28. [36]

    : Beyond yes and no: Improving zero-shot llm rankers via scoring fine-grained relevance labels

    Zhuang H., Qin Z., Hui K., Wu J., Yan L., Wang X., Bendersky M. : Beyond yes and no: Improving zero-shot llm rankers via scoring fine-grained relevance labels. arXiv preprint arXiv:2310.14122 (2023)

    2023 arXiv

  29. [37]

    : Can chatgpt reproduce human-generated labels? a study of social computing tasks

    Zhu Y., Zhang P., Haq E.-U., Hui P., Tyson G. : Can chatgpt reproduce human-generated labels? a study of social computing tasks. arXiv preprint arXiv:2304.10145 (2023)

    2023 arXiv

  30. [38]

    write newline

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  31. [39]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry.original add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.s...

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