An endpoint point-relation classifier followed by decoding to interval relations achieves 70.1% temporal awareness on TempEval-3, setting a new state-of-the-art for the full set of fine-grained temporal relations.
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Math reasoning gains in LLMs rarely transfer to general domains; RL tuning generalizes while SFT causes forgetting and representation drift.
UltraChat supplies 1.5 million high-quality multi-turn dialogues that, when used to fine-tune LLaMA, produce UltraLLaMA, which outperforms prior open-source chat models including Vicuna.
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Looking for the Bottleneck in Fine-grained Temporal Relation Classification
An endpoint point-relation classifier followed by decoding to interval relations achieves 70.1% temporal awareness on TempEval-3, setting a new state-of-the-art for the full set of fine-grained temporal relations.
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Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
Math reasoning gains in LLMs rarely transfer to general domains; RL tuning generalizes while SFT causes forgetting and representation drift.
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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
UltraChat supplies 1.5 million high-quality multi-turn dialogues that, when used to fine-tune LLaMA, produce UltraLLaMA, which outperforms prior open-source chat models including Vicuna.