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cs.CL2025
ConsintBench: Evaluating Language Models on Real-World Consumer Intent Understanding
Xiaozhe Li, TianYi Lyu, Siyi Yang +6
Understanding human intent is a complex, high-level task for large language models (LLMs), requiring analytical reasoning, contextual interpretation, dynamic information aggregatio…
cs.CL2025
TrInk: Ink Generation with Transformer Network
Zezhong Jin, Shubhang Desai, Xu Chen +8
In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input…