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Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

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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.

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Authors: Aimen Boukhari
Released: 2026-06-05 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, text, under, token, line, rate, alone. Source: arxiv cs.CL (NLP).
arXiv: 2606.05173

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