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Stanford NLP announces that a linguistics-informed NLP paper titled 'The Imperfective Paradox in LLMs' has won a Best Paper Award at ACL 2026, encouraging more detailed linguistic evaluation of large language models.
Introduces ToolGrad, an agentic framework that generates, evaluates, and refines tool-use trajectories using textual 'gradients', achieving near 100% pass rate and lower cost for dataset generation. Accepted at ACL 2026.
This paper presents Structure-Guided Entity Resolution (SGER), a framework that fine-tunes LLMs through curriculum learning for robust person name matching in linguistically diverse contexts, achieving 99.02% accuracy on Indian identity data and deployed at Dream11.
A personal project led to an ACL 2026 paper introducing TIME, a method training Qwen3 models to engage in short, context-triggered thinking rather than excessive reasoning. The work uses QLoRA and a four-phase curriculum, with all data and code released open-source.