# BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data

arXiv: https://arxiv.org/abs/2303.07902 · HTML: https://mlsanity.com/paper/2303.07902/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2023.

## Built on by (most cited first, 15 of 15)

- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models](https://mlsanity.com/paper/2405.09589/) (2024)
- [Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning](https://mlsanity.com/paper/2309.11500/) (2023)
- [T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2404.17806/) (2024)
- [GLAP: General contrastive audio-text pretraining across domains and languages](https://mlsanity.com/paper/2506.11350/) (2025)
- [Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data](https://mlsanity.com/paper/2410.02056/) (2024)
- [Bridging Language Gaps in Audio-Text Retrieval](https://mlsanity.com/paper/2406.07012/) (2024)
- [Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation](https://mlsanity.com/paper/2511.16757/) (2025)
- [Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning](https://mlsanity.com/paper/2304.05600/) (2023)
- [Sample-efficient Integration of New Modalities into Large Language Models](https://mlsanity.com/paper/2509.04606/) (2025)
- [Leveraging Prediction Entropy for Automatic Prompt Weighting in Zero-Shot Audio-Language Classification](https://mlsanity.com/paper/2601.05011/) (2026)
- [Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods](https://mlsanity.com/paper/2603.25767/) (2026)
- [Testing chatbots on the creation of encoders for audio conditioned image generation](https://mlsanity.com/paper/2509.09717/) (2025)
- [ASK: Adaptive Self-improving Knowledge Framework for Audio Text Retrieval](https://mlsanity.com/paper/2512.19703/) (2025)
- [SLAP: Scalable Language-Audio Pretraining with Variable-Duration Audio and Multi-Objective Training](https://mlsanity.com/paper/2601.12594/) (2026)

## Abstract (arXiv)

Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing methods incorporate the visual modality as a pivot for audio-text pre-training, which inevitably induces data noise. In this paper, we propose to utilize audio captioning to generate text directly from audio, without the aid of the visual modality so that potential noise from modality mismatch is eliminated. Furthermore, we propose caption generation under the guidance of AudioSet tags, leading to more accurate captions. With the above two improvements, we curate high-quality, large-scale parallel audio-text data, based on which we perform audio-text pre-training. We comprehensively demonstrate the performance of the pre-trained model on a series of downstream audio-related tasks, including single-modality tasks like audio classification and tagging, as well as cross-modal tasks consisting of audio-text retrieval and audio-based text generation. Experimental results indicate that our approach achieves state-of-the-art zero-shot classification performance on most datasets, suggesting the effectiveness of our synthetic data. The audio encoder also serves as an efficient pattern recognition model by fine-tuning it on audio-related tasks. Synthetic data and pre-trained models are available online.