# IncompeBench: A Permissively Licensed, Fine-Grained Benchmark for Music Information Retrieval

arXiv: https://arxiv.org/abs/2602.11941 · HTML: https://mlsanity.com/paper/2602.11941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [LP-MusicCaps: LLM-Based Pseudo Music Captioning](https://mlsanity.com/paper/2307.16372/) (2023)
- [FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://mlsanity.com/paper/2403.15246/) (2024)
- [The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation](https://mlsanity.com/paper/2311.10057/) (2023)
- [UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor](https://mlsanity.com/paper/2406.06519/) (2024)
- [From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities](https://mlsanity.com/paper/2412.11694/) (2024)
- [CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages](https://mlsanity.com/paper/2502.10362/) (2025)
- [Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges](https://mlsanity.com/paper/2504.15205/) (2025)
- [MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark](https://mlsanity.com/paper/2505.11651/) (2025)
- [Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval](https://mlsanity.com/paper/2410.03264/) (2024)
- [SLAP: Siamese Language-Audio Pretraining Without Negative Samples for Music Understanding](https://mlsanity.com/paper/2506.17815/) (2025)
- [GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models](https://mlsanity.com/paper/2506.17886/) (2025)

## Abstract (arXiv)

Multimodal Information Retrieval has made significant progress in recent years, leveraging the increasingly strong multimodal abilities of deep pre-trained models to represent information across modalities. Music Information Retrieval (MIR), in particular, has considerably increased in quality, with neural representations of music even making its way into everyday life products. However, there is a lack of high-quality benchmarks for evaluating music retrieval performance. To address this issue, we introduce IncompeBench, a carefully annotated benchmark comprising 1,574 permissively licensed, high-quality music snippets, 500 diverse queries, and over 125,000 individual relevance judgements. These annotations were created through the use of a multi-stage pipeline, resulting in high agreement between human annotators and the generated data. The resulting datasets are publicly available at https://huggingface.co/datasets/mixedbread-ai/incompebench-strict and https://huggingface.co/datasets/mixedbread-ai/incompebench-lenient with the prompts available at https://github.com/mixedbread-ai/incompebench-programs.