AI & Domain Specialization

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Thursday, 16 July 2026

NAVER LABS System Re-implementation for the IWSLT 2026 Instruction-Following Task

arXiv cs.CL 18 hours ago

NAVER LABS implemented a speech-to-text instruction-following system for the IWSLT 2026 shared task using SeamlessM4T-v2-large for speech encoding and Qwen3-4B-Instruct as the language model. The system uses a three-stage pipeline with projector alignment, text-only LoRA pre-training, and multimodal merging, supported by 100,000 synthetic instruction-following examples across ten task types. The model achieved COMET 0.781 on English-to-Chinese speech translation and BERTScore-F1 0.346 on English spoken question answering.

AI & Audio Processing AI Code Generation AI Datasets & Annotation AI & Domain Specialization

Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation

arXiv cs.CL 18 hours ago

Researchers introduced TTARAG, a test-time adaptation method that adjusts language model parameters during inference to improve Retrieval-Augmented Generation system performance in specialized domains. The method was evaluated across six specialized domains and showed substantial performance improvements over baseline RAG systems. This approach enables RAG systems to automatically adapt to target domains when distribution shifts occur, reducing the performance gap between general and specialized applications.

AI Adaptation & Learning AI & Domain Specialization Retrieval-Augmented Generation