| --- |
| 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 |
|
|
| [](https://github.com/ethicalabs-ai/Echo-DSRN/) |
| [](https://opensource.org/licenses/Apache-2.0) |
| [](https://www.python.org/downloads/) |
| [](https://openaire-2026.ethicalabs.ai/) |
| [](https://innovation.openaire.eu/component/content/article/openaire-ai-hackathon.html) |
|
|
| [](https://www.youtube.com/watch?v=9LHmmzLK3zs) |
|
|
| [**▶️ Watch the Video**](https://www.youtube.com/watch?v=9LHmmzLK3zs) |
|
|
| 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](https://innovation.openaire.eu/component/content/article/openaire-ai-hackathon.html), co-organised by OpenAIRE and Alien Intelligence — a 12-week open science build challenge. |
|
|
| **[🔴 Live Demo → openaire-2026.ethicalabs.ai](https://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](https://graph.openaire.eu) 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 |
|
|
| ```json |
| { |
| "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](https://graph.openaire.eu/) 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](https://github.com/ethicalabs-ai/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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
