A two-stage replay-based post-training method corrects ASR timestamp drift across non-speech gaps while preserving recognition far better than ordinary timestamp fine-tuning.
LoRA: Low-rank adaptation of large language models,
3 Pith papers cite this work. Polarity classification is still indexing.
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LoRA adaptation of a frozen SigLIP backbone helps most on synthetic BIQA benchmarks when reference-level splits expose weak frozen features, while image-level splits inflate scores and hide the need for adaptation.
Translates SemEval-2010 Task 8 to Romanian and evaluates Gemma 31B prompting and QLoRA fine-tuning against encoder baselines, finding fine-tuning reduces the cross-lingual gap to 1.4pp while smaller models perform within 1-4pp.
citing papers explorer
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REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
A two-stage replay-based post-training method corrects ASR timestamp drift across non-speech gaps while preserving recognition far better than ordinary timestamp fine-tuning.
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Parameter-Efficient Adaptation of a Multi-Stream Vision-Language Framework for Blind Image Quality Assessment
LoRA adaptation of a frozen SigLIP backbone helps most on synthetic BIQA benchmarks when reference-level splits expose weak frozen features, while image-level splits inflate scores and hide the need for adaptation.
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Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
Translates SemEval-2010 Task 8 to Romanian and evaluates Gemma 31B prompting and QLoRA fine-tuning against encoder baselines, finding fine-tuning reduces the cross-lingual gap to 1.4pp while smaller models perform within 1-4pp.