FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following
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Computer Science > Computation and Language
Title:FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following
Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.26819 [cs.CL] |
| (or arXiv:2606.26819v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26819
arXiv-issued DOI via DataCite (pending registration)
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