JensCS/top-ml-conference-papers-2023

Top ML Conference Papers 2023

A dataset of 7,035 papers from ACL 2023, CVPR 2023, ICLR 2023, and NeurIPS 2023, processed with KNOWHERE — a document understanding pipeline that prepares unstructured data for AI agents.

GitHub Repo: https://github.com/Ontos-AI/knowhere

Conference	Papers	Domain
ACL 2023	1,044	Natural Language Processing
CVPR 2023	2,248	Computer Vision
ICLR 2023	545	Machine Learning
NeurIPS 2023	3,198	Machine Learning

Construction

Built with KNOWHERE:

1. PDF Parsing — KNOWHERE parses each PDF into structured chunks (chunks.json): text blocks with section paths. The dataset retains each chunk's content and path.
2. Hierarchy Extraction — KNOWHERE outputs a doc_nav.json section tree per paper, with title, path, level, summary, chunk_count, and recursive children.
3. Agentic Extraction — An LLM pipeline classifies papers into 4 types and extracts fields from KNOWHERE's parsed chunks and section hierarchy.
4. Assembly — Extraction fields + KNOWHERE chunks + hierarchy merge into unified records.

Applications

• Literature analysis and survey support — structured extraction fields (method, results, baselines) enable systematic literature review, cross-conference trend tracking, and automated survey generation
• Benchmark for PDF parsing & scientific understanding — evaluate document parsing pipelines and information extraction models on real academic papers with ground-truth hierarchy and typed fields
• Foundation for AI for Science experiments — structured paper representations serve as input for downstream tasks such as paper generation, research idea proposal, and citation-aware knowledge construction

Field Schema

All 24 extraction fields appear in every record. Fields not applicable to a paper's type are empty strings (or empty lists).

Common (all papers)

Field	Type	Description
paper_title	string	Verbatim title
authors	list[string]	Author names
affiliation	string	First author's institution
research_domain	string	AI/CS subfield
task_type	string	Specific technical task
core_problem	string	Unresolved limitation
key_innovation	string	Primary contribution
code_repository	string	Code URL (if stated)

model_architecture (1,004 papers)

Field	Description
model_name	Proposed model name
architecture_type	Design paradigm (e.g., Transformer)
model_size_parameters	Parameter count
training_evaluation_dataset	Datasets used
key_results	Quantitative results

method_and_pipeline (5,196 papers)

Field	Description
method_name	Proposed method name
method_summary	Core mechanism
baseline_models	list[string] — baselines compared
key_results	Advantage over baselines

theory_and_analysis (798 papers)

Field	Description
analysis_target	Object/phenomenon studied
theoretical_tools	Proof techniques
key_findings	Theorems or conclusions
prior_work_comparison	Comparison to prior results
limitations	Assumptions or scope

agent_system (37 papers)

Field	Description
agent_framework_name	Proposed framework name
environment_or_tools	Environments/tools used
planning_mechanism	Reasoning strategy
eval_benchmark	Evaluation benchmarks
key_results	Benchmark performance

Structural & Metadata

Field	Type	Description
paper_type	string	Classification key
type_name	string	Human-readable type name
extraction_quality	float	Non-null field fraction
total_chars	int	Paper character count
conference	string	Source conference
chunks	list[{content, path}]	KNOWHERE text chunks
hierarchy	list[{title, path, level, summary, chunk_count}]	KNOWHERE section tree

Formats

• JSONL — Nested hierarchy tree with recursive children, human-readable.
• Parquet — Columnar format, Zstd compression; hierarchy flattened to depth-first list.

Loading

from datasets import load_dataset

ds = load_dataset("JensCS/top-ml-conference-papers-2023", split="train")

print(ds[0]["paper_title"])
print(ds[0]["conference"], ds[0]["paper_type"])

# Type-specific fields
agent = ds.filter(lambda x: x["paper_type"] == "agent_system")
print(agent[0]["agent_framework_name"])

# KNOWHERE hierarchy
for sec in ds[0]["hierarchy"]:
    depth = "  " * (sec["level"] - 1)
    print(f"{depth}├─ {sec['title']}")

Pipeline

KNOWHERE → Parse → Structure → Build Memory → Agentic extraction → Dataset assembly.

License

CC BY 4.0. Original papers retain their respective copyrights.