Model merging aims to build a multi-task model cheaply by combining the weights of individual task-specific models. To perform well across multiple tasks, most existing merging methods use an additional dataset to find the coefficients for the best linear combination of task-specific weight updates. However, we identif...
Sin-Han Yang, Shih-Cheng Huang, Chieh-Yen Lin et al.· 0 citations
Model merging offers a promising solution for combining multiple fine-tuned checkpoints into a single model through parameter arithmetic. However, finding optimal merging coefficients requires an extensive search that becomes prohibitively expensive as models scale in both size and number, due to high memory requiremen...
Shih-Cheng Huang, Zhi Rui Tam, Chieh-Yen Lin et al.· 0 citations
Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on response-level confidence, which estimates the correctness of a single generated output. However, this formulation is misaligned with many practical settings where the central qu...
Experiments show that the proposed Preference Vector framework improves helpfulness without excessive conservatism, allows smooth control over preference trade-offs, and supports scalable multi-preference alignment.
The Teaching Monster Challenge is introduced, the first instructional video generation benchmark to treat the learner persona as an explicit evaluation criterion and release the benchmark, rubric, and human judgments as a testbed for both teaching systems and automatic judges.
Yi-Cheng Lin, Yu-Kai Guo, Szu-Chi Chen et al.· 0 citations
A reinforcement learning framework that jointly trains tool creation and tool use inside a single policy, with three separate reward axes that catch schema, code, and outcome failures independently, so each failure mode contributes its own gradient.
Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen et al.· 2 citations
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