Synthetic LLM-generated conversations improve ASR training for low-resource languages
Researchers propose a pipeline that uses LLMs to generate scenario-level dialogues and TTS to synthesize multi-speaker audio, creating simulated conversational training data for ASR systems. Evaluated on the Hungarian BEA-Dialogue benchmark, a model trained on 67 hours of real plus 636 hours of synthetic data outperforms a zero-shot model trained on 2,700 hours of real Hungarian speech. The study tests five LLM families under multiple budget and mixing configurations using a FastConformer-Large backbone, finding that generator choice and data composition significantly affect gains.
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BayLing-Duplex: Native full-duplex speech dialogue using a single autoregressive LLM
Researchers introduce BayLing-Duplex, a speech language model that achieves native full-duplex interaction — simultaneous listening and speaking — using a single autoregressive LLM with no auxiliary VAD or turn-taking module. Built by fine-tuning GLM-4-Voice on 400K samples plus a lightweight DPO stage, it reaches 92% turn-taking success and 100% interruption success on InstructS2S-Eval, and improves speech-response quality substantially over Moshi. The approach adds only special tokens to the standard vocabulary, making it portable across LLM architectures without architectural changes.
RL-based alignment improves interactivity in full-duplex spoken dialogue models
Researchers propose a post-training alignment method using reinforcement learning to improve interactivity in full-duplex spoken dialogue models, which can listen and speak simultaneously. The method addresses four canonical axes of interactivity—pause handling, turn-taking, backchanneling, and user interruption—each with axis-specific reward functions, plus an LLM-based reward to prevent semantic degradation. The approach is applied to two open-source models, Moshi and PersonaPlex, showing consistent improvements in both offline and real-time multi-turn evaluation.
Synthetic data generation method enables small LLMs to match large models on Text-To-Cypher tasks
A new arXiv paper presents an automatic synthetic data generation method for fine-tuning small LLMs on Text-To-Cypher (Text2Cypher) parsing, enabling natural language interfaces to property graph databases. Experiments across major Text-To-Cypher benchmarks show that small fine-tuned models can compete with much larger proprietary models. The approach is positioned as a solution for local deployment scenarios requiring data sovereignty without expensive annotation.
Synthetic data bootstrapping and LoRA fine-tuning for Q'eqchi' Mayan NMT without web scraping
Researchers introduce a data synthesis methodology for low-resource neural machine translation of Q'eqchi' Mayan, converting community-sourced dictionaries into a synthetic parallel corpus to avoid scraping target-language data. Using LoRA adapters on mT5-base, the approach achieves BLEU 42.02 on in-domain evaluation but only 0.59 against organic text, revealing a structural-semantic gap. An ablation with multi-task learning produced negative transfer, suggesting LoRA capacity limits conflict with auxiliary objectives. The study concludes synthetic bootstrapping is effective for structural priming but requires authentic data for semantic refinement via curriculum learning.
Synthetic linguistic reasoning traces improve low-resource machine translation via in-context learning
Researchers propose a pipeline that generates step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks to assist LLMs in translating extremely low-resource languages. Evaluated on Xibe and Chintang across ICL, SFT, and RFT settings, the traces prove most effective as inference-time guidance rather than training data. Models can leverage reliable grammatical analyses at inference time but struggle to learn to generate accurate traces themselves, identifying trace generation quality as the key bottleneck.
Audio-LLM-based data filtering for speech-to-speech translation via Rank-to-Distill
A new arXiv paper proposes using audio large language models to filter noisy training data for end-to-end speech-to-speech translation (S2ST). The authors introduce a two-stage Rank-to-Distill strategy: a lightweight ranker generates pseudo-labels from noisy speech pairs, which then supervise an audio-LLM to make keep/drop decisions directly from raw audio. Experiments on CVSS-C and SpeechMatrix benchmarks show up to +1.4 ASR-BLEU improvement over unfiltered baselines.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
AlignAtt4LLM adapts simultaneous speech translation policy to decoder-only LLMs for IWSLT 2026
Researchers present AlignAtt4LLM, a simultaneous speech translation system for IWSLT 2026 covering English to German, Italian, and Chinese. The system cascades Qwen3-ASR for incremental transcription with Gemma-4 E4B-it for translation, applying a novel AlignAtt policy adapted for decoder-only LLMs that lack encoder-decoder cross-attention. Key contributions include explicit source span prompting, offline alignment head selection, and query/key capture to recover a usable attention-based read/write policy. The system outperforms IWSLT 2026 baselines for European language pairs in both low- and high-latency regimes.