Datasets:

ethicalabs
/

Research-Intent-Judge

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
  - multilingual
tags:
  - research-intent
  - llm-as-judge
  - chatml
  - openaire
pretty_name: Research Intent — LLM-as-Judge
configs:
  - config_name: default
    data_files:
      - split: Bonsai_8B_gguf
        path: llm_judge_Bonsai-8B-gguf.jsonl
      - split: GLM_4.7_Flash_GGUF
        path: llm_judge_GLM-4.7-Flash-GGUF.jsonl
      - split: Gemma_4_12B_it_GGUF
        path: llm_judge_Gemma-4-12B-it-GGUF.jsonl
      - split: Gemma_4_26B_A4B_it_GGUF
        path: llm_judge_Gemma-4-26B-A4B-it-GGUF.jsonl
      - split: Gemma_4_31B_it_GGUF
        path: llm_judge_Gemma-4-31B-it-GGUF.jsonl
      - split: Gemma_4_E2B_it_GGUF
        path: llm_judge_Gemma-4-E2B-it-GGUF.jsonl
      - split: Gemma_4_E4B_it_GGUF
        path: llm_judge_Gemma-4-E4B-it-GGUF.jsonl
      - split: LFM2_24B_A2B_GGUF
        path: llm_judge_LFM2-24B-A2B-GGUF.jsonl
      - split: LFM2_8B_A1B_GGUF
        path: llm_judge_LFM2-8B-A1B-GGUF.jsonl
      - split: Nemotron_3_Nano_30B_A3B_GGUF
        path: llm_judge_Nemotron-3-Nano-30B-A3B-GGUF.jsonl
      - split: Phi_4_mini_instruct_GGUF
        path: llm_judge_Phi-4-mini-instruct-GGUF.jsonl
      - split: Qwen3.5_35B_A3B_GGUF
        path: llm_judge_Qwen3.5-35B-A3B-GGUF.jsonl
      - split: Qwen3.5_4B_FP16_vLLM
        path: llm_judge_Qwen3.5-4B-FP16-vLLM.jsonl
      - split: Qwen3.5_4B_GGUF
        path: llm_judge_Qwen3.5-4B-GGUF.jsonl
      - split: Qwen3.6_27B_GGUF
        path: llm_judge_Qwen3.6-27B-GGUF.jsonl
      - split: Qwen3.6_35B_A3B_GGUF
        path: llm_judge_Qwen3.6-35B-A3B-GGUF.jsonl
      - split: SmolLM3_3B_GGUF
        path: llm_judge_SmolLM3-3B-GGUF.jsonl
      - split: gemini_3.5_flash
        path: llm_judge_gemini-3.5-flash.jsonl
      - split: gpt_oss_120b_mxfp_GGUF
        path: llm_judge_gpt-oss-120b-mxfp-GGUF.jsonl
      - split: gpt_oss_20b_mxfp4_GGUF
        path: llm_judge_gpt-oss-20b-mxfp4-GGUF.jsonl
      - split: granite_4.0_h_tiny_GGUF
        path: llm_judge_granite-4.0-h-tiny-GGUF.jsonl
      - split: human_annotations
        path: human_annotations.jsonl

Research Intent — LLM-as-Judge

▶️ Watch the Video

LLM-as-Judge annotations for research paper intent classification, collected through the Echo-DSRN collaborative platform during the OpenAIRE AI Hackathon 2026.

The dataset has one split per judge model (Gemma_4_E4B_it_GGUF, Qwen3.6_35B_A3B_GGUF, Bonsai_8B_gguf, ...) plus a human_annotations split with curator annotations. Split names use underscores in place of the dashes in model names (HF does not allow dashes in split names).

Each record is a single judge verdict from one model on one paper, in ChatML conversation format with the judge's reasoning (rationale) included both inline in the assistant message and as a separate reasoning field.

OpenAIRE AI Hackathon 2026 🇪🇺

This dataset is part of ethicalabs.ai's entry in the OpenAIRE AI Hackathon 2026, co-organised by OpenAIRE and Alien Intelligence — a 12-week open science build challenge.

🔴 Live Demo → openaire-2026.ethicalabs.ai

The live application classifies research papers in real time using this model, with publication metadata streamed from the OpenAIRE Graph API under CC BY 4.0.

Labels

Methodology — introduces a new algorithm, model architecture, or method
Dataset — primarily contributes a new dataset, benchmark, or annotation scheme
Review — survey, systematic review, or position paper
Applied — applies existing methods to a domain problem
Theoretical — mathematical or formal analysis
Unclassifiable — garbled, non-English, or non-academic content

Data structure

{
  "doi": "10.48550/arXiv.2106.01345",
  "title": "...",
  "description": "abstract text",
  "initial_intent": "Methodology",
  "messages": [
    {"role": "system", "content": "You are a multilingual research paper intent classifier..."},
    {"role": "user", "content": "Classify the research intent of this paper:\n\nTitle: ...\nAbstract: ..."},
    {"role": "assistant", "content": "The paper introduces a novel...\n\nDataset"}
  ],
  "reasoning": "The paper introduces a novel...",
  "model_prediction": "Dataset",
  "is_flagged": false,
  "flag_reason": null,
  "confidence": "high"
}

messages — ChatML conversation with inline reasoning in the assistant turn
reasoning — the judge's rationale as a standalone field
model_prediction — the label proposed by the judge
is_flagged / flag_reason — quality-issue signal (garbled text, language mismatch)
confidence — high / medium / low

Splits

One split per judge model (21 models, ~10k records each) plus human_annotations (46 curator annotations). Records within a split are per-paper single verdicts; a paper appears in every model split that judged it.

Provenance

Papers were collected from arXiv and OpenAIRE via the OpenAIRE Graph and judged by 20+ open-weight LLMs. Underperforming models (e.g. DeepSeek-Qwen3-8B, Kurtis-E1.1, Ministral-3-3B, Jan-v1-4B) are excluded. MTP and non-MTP variants of the same model are merged (they produce identical predictions).

Export tooling: scripts/export_llm_judge_dataset.py in the OpenAIRE-AI-Research-Evaluator repository.

License

CC-BY 4.0. Paper metadata originates from OpenAIRE Graph (CC BY 4.0).

Citation

If you reuse this work, please cite:

@software{echo_dsrn_research_intent_clf,
  author = {Massimo Roberto Scamarcia},
  title = {OpenAIRE Graph Evaluator: Research paper classifier and LLM-as-Judge pipeline},
  year = {2026},
  url = {https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026}
}