Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning
arXiv:2606.05173v1 Announce Type: new Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure. Inspired by the success of Joint Embedding Predictive Architectures (JEPA) (LeCun, 2022) in vision and audio, we propose a hybrid pre-training objective that combines a JEPA-style latent-space prediction loss with a standard MLM objective over a single shared encoder. A learnable scalar parameter continuously bal
The AI research community is actively exploring novel pre-training objectives to overcome limitations of current models like BERT, driven by a deeper understanding of semantic representation needs.
This research outlines a methodology to develop more semantically robust and efficient AI language models, which could significantly enhance their capabilities beyond surface-level text understanding.
Pre-training methodologies for large language models may evolve to incorporate latent-space prediction alongside masked language modeling, potentially leading to more powerful and generalizable AI systems.
- · AI research institutions
- · Developers of large language models
- · Any industry relying on advanced NLP
- · Companies relying on less sophisticated NLP solutions
- · Models heavily dependent on superficial text analysis
Improved performance and efficiency of AI language models in complex tasks.
Acceleration of AI agent capabilities due to enhanced semantic understanding and reasoning.
Broader adoption of AI in applications requiring nuanced language comprehension, expanding the scope of automation.
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