KVpress Leaderboard

• KVpress Leaderboard is a framework that curates empirical progress metrics from scientific publications in data-driven fields like KGQA and AI/NLP.
• It employs advanced methodologies including Transformer-based document classification and LLM finetuning to extract structured (Task, Dataset, Metric, Score) data.
• The platform significantly enhances reproducibility and transparency by standardizing benchmark evaluations and addressing fragmented reporting.

KVpress Leaderboard is a technical framework and online resource for aggregating, extracting, and curating empirical progress metrics—typically structured as (Task, Dataset, Metric, Score) quadruples—from scientific articles within data-driven fields such as Knowledge Graph Question Answering (KGQA) and AI/NLP. It functions as both a community-maintained leaderboard hub and an automated extraction system powered by advanced natural language understanding and LLM approaches. Its development addresses longstanding concerns about comparability, reproducibility, and transparency in reporting state-of-the-art results.

1. Origins and Motivations

The inception of KVpress Leaderboard is rooted in the need for reliable, publicly accessible repositories of system evaluations in algorithmic research. The proliferation of benchmark datasets (e.g., QALD-8/9, LC-QuAD 1.0/2.0 in KGQA) and heterogeneous evaluation protocols led to fragmented result tables, incomplete comparisons, and lack of a central "source of truth" (Perevalov et al., 2022). This absence aggravated risks of replication crises and eroded trust in reported scientific progress. The KVpress initiative provides a curated, extensible platform for tracking evaluation results, system metadata, and reproducibility information across domains.

2. Architecture and Curation Workflow

KVpress Leaderboard is hosted as an open-source GitHub Pages resource (https://kgqa.github.io/leaderboard/), supporting submission and validation workflows structured around the following:

• Submission Process: Contributors fork the repository and add system entries in dataset-specific YAML/JSON files. Each entry specifies system name, publication reference, evaluation metrics and scores, and public code/demo links.
• Validation: Core maintainers cross-verify reported numbers against primary sources, standardize metric definitions, and merge valid pull requests.
• Interface Features: Tables provide sortable columns (System, Metric, Score, Year, Link) and multidimensional filtering (by dataset, KG, metric, year). Version logs ensure full auditability.
• Dataset Support: Coverage now spans 34 datasets over 5 knowledge graphs, with focused depth on DBpedia, Wikidata, Freebase, WikiMovies, and EventKG.

This principled curation ensures not only up-to-date benchmarks but also historical continuity and reproducibility metadata.

3. Methodologies for Leaderboard Extraction

Automating the generation of scientific leaderboards from research articles hinges on robust information extraction (IE) systems. KVpress integrates and extends methodologies such as:

• TDMS-IE Framework: Utilizes PDF parsing (via GROBID) and table structure analysis, followed by Transformer-based Document Classification (DocTAET) and Score Context (SC) modules for predicting which <Task, Dataset, Metric> tuples are reported, and extracting best scores via NLI fine-tuning (Hou et al., 2019). Achieves micro-F1 scores of ~67 for triple identification, with score extraction as the limiting step.
• Instruction Finetuning of LLMs: Adopts sequence-to-sequence finetuning (e.g., FLAN-T5) with multi-template context+question prompts derived from SQuAD and DROP (Kabongo et al., 2024). Context windows are formed using DocTAET methods (title, abstract, experimental setup, table info). Finetuned models discern both presence/absence of leaderboards (∼96% accuracy) and extract quadruples, with partial-match F1 around 28 for overall extraction (score remains the hardest token to ground).
• Parameter-Efficient LLM Adaptation: Implements QLoRA-based fine-tuning for open-source models (e.g., Mistral 7B, Llama-2) and prompt engineering for proprietary models (GPT-4-Turbo/o). Context selection is empirically optimized (DocTAET for open-source, DocREC/DocFULL for GPT-4.o). Fuzzy string-matching is employed in the post-filter stage for aligning extractions to ground truth (Kabongo et al., 2024).

Key extraction strategies thus blend pre-filtered context selection, prompt diversity, and model-specific optimizations for heightened reliability and coverage.

4. Evaluation Protocols, Metrics, and Results

Evaluation within KVpress Leaderboard adheres to established IR and IE standards:

• Metrics: Precision ($P$), Recall ($R$), F1-score ($F_1$), Exact Match (EM), and Hit@k ($H@k$), with micro- and macro-averaging options (Perevalov et al., 2022). For quadruple extraction: $F_1 = \frac{2 \cdot P \cdot R}{P + R}$.
• Corpus Coverage and Statistics: 100 publications, 98 distinct KGQA systems over 34 datasets; 7,987 leaderboard papers in training, 241 leaderboard papers in test sets (Kabongo et al., 2024).
• Quantitative Outcomes:
  • SOTA FLAN-T5 achieves ~28 partial F1 in quadruple extraction; task and metric identification are strongest, with score extraction still below 1 F1 (Kabongo et al., 2024).
  • Open-source LLMs (Mistral 7B + DocTAET) yield up to 27.67 F1, while GPT-4.o with DocREC attains 28.62 F1 (Kabongo et al., 2024).
  • Binary detection accuracy of "leaderboard present or unanswerable" exceeds 96%.
  • For KGQA, F1-score variance across systems rarely exceeds 1%, except in cases tied to live demo drift (Perevalov et al., 2022).

The extraction and evaluation methodology is actively refined based on empirical error analyses—table mis-parsing, spurious side-benchmarks, and context truncation are dominant failure modes.

Model	Context	Partial F1 (mean ± std)
Mistral 7B	DocTAET	27.67 ± 1.41
GPT-4.o	DocREC	28.62 ± 0.28
FLAN-T5-Large	DocTAET	~28

5. Identified Challenges and Recommendations

Analysis of leaderboard resource deployments and extraction reveals several persistent issues:

• Historical Omissions: 72% of papers omit relevant prior SOTA results, distorting fair comparison (Perevalov et al., 2022).
• Reproducibility Gaps: Only 24% of systems provide public source code; less than 16% have operational demos.
• Evaluation Pitfalls: Inconsistent splits, ad-hoc answer normalization, aggregation/filter divergence (Perevalov et al., 2022).
• Extraction Limitations: Numeric score extraction remains weakest, regular table-parsing errors, and ambiguity in benchmark identification, especially variants and side-results (Hou et al., 2019, Kabongo et al., 2024).

Recommendations include mandatory publication of evaluation protocols, standardized answer normalization, online benchmarking (e.g., via GERBIL-QA), periodic taxonomy expansion, active-learning calibration, and potential integration with multimodal extraction approaches and knowledge graph platforms (e.g., ORKG).

6. Implications and Future Directions

KVpress Leaderboard and its extraction systems drive several key impacts and ongoing research avenues:

• Replication Crisis Mitigation: Centralized, versioned leaderboard resources forestall loss of evaluation provenance and result drift.
• Community Engagement: Open curation and annotation by research working groups facilitates sustainable, living benchmarks.
• Methodological Rigor: Objective performance assessment promotes sound experimental design, discourages overuse of leaderboard features without empirical justification.
• Automation Advances: Instruction finetuning and context-optimized LLMs establish practical baseline systems for open-world information extraction in scientific domains.

Future directions involve dynamic hybrid context selection, ensemble model strategies, integration of table-aware encoders, and extension into multimodal scientific publication formats. Continued empirical validation and taxonomy curation are essential to maintaining leaderboard reliability and field-wide comparability.

7. Comparative Perspective and Generalization

While originated in KGQA and NLP, KVpress-style leaderboard resources and extraction pipelines generalize to broader empirical science domains. The principles of transparent curation, robust extraction, and open evaluation architectures can be adapted to new task/dataset/metric regimes. Lessons from leaderboard design in gamification contexts emphasize that more complex ranking systems are not automatically beneficial; a lightweight, data-driven approach tailored to motivational and resource constraints is preferred (Pedersen et al., 2017).

In summary, KVpress Leaderboard represents a convergence of community norms, technical IE innovation, and infrastructural transparency for tracking, comparing, and reproducibly advancing scientific progress across data-driven disciplines (Perevalov et al., 2022, Hou et al., 2019, Kabongo et al., 2024, Kabongo et al., 2024).