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cs.CL2026
To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation
Jiuheng Lin, Chen Zhang, Yansong Feng
While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets overlap with pretraining or SF…
cs.CL2026
Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion
Chen Zhang, Jiuheng Lin, Zhiyuan Liao +1
Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although Proxy Tuning offers a logit…
cs.CL2026
An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
Yinhan Lu, Gaganpreet Jhajj, Chen Zhang +2
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent…