Enhancing Multilingual Reasoning via Steerable Model Merging
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Computer Science > Computation and Language
Title:Enhancing Multilingual Reasoning via Steerable Model Merging
Abstract:Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages.
| Comments: | 12 pages, 7 figures, 8 tables. Accepted by ACL2026 Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.19002 [cs.CL] |
| (or arXiv:2606.19002v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19002
arXiv-issued DOI via DataCite (pending registration)
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