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FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following

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This paper describes our submission to the IWSLT 2026 Instruction Following shared task. SpeechLLMs are developed for both short-form and long-form speech instruction following under constrained settings. For the short track, strong performance is achieved on MCIF, with a SIFS score of 2.0708. For the long track, three speech segmentation methods are explored, and the HIFS score is introduced to account for unstable long-form generation. Experimental results show that fixed 30-second segmentation provides the most robust long-form performance, achieving the highest HIFS score of 2.0663. Further analysis shows that hallucination mainly manifests as repetitive insertions in generated outputs, substantially affecting ASR and SSUM, while short-form capabilities are largely retained after long-form extension.

Zhihang Xie, Marco Gaido, Sara Papi, Matteo Negri, Luisa Bentivogli• 2026

Related benchmarks

TaskDatasetResultRank
Speech Question AnsweringMCIF short-form
BERTScore (SQA)45.13
10
Speech TranslationMCIF short-form
ST COMET78.69
8
Automatic Speech RecognitionMCIF short-form
ASR Accuracy88.77
4
ACHAP TrackIWSLT long-form EN-EN 2026
WER20
3
ACHAP TrackIWSLT long-form EN-DE 2026
COMET0.69
3
ACHAP TrackIWSLT long-form EN-IT 2026
COMET0.735
3
ACHAP TrackIWSLT long-form EN-ZH 2026
COMET0.698
3
Automatic Speech RecognitionIWSLT long-form EN-EN 2026
WER12.6
3
Quality EstimationIWSLT long-form EN-DE 2026
Accuracy50.1
3
Quality EstimationIWSLT long-form EN-ZH 2026
Accuracy65.8
3
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