main webpage W Topic Text Mining arXiv (Cornell University) Text2Grad: Reinforcement Learning from Natural Language Feedback 2025 Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving… Article Open Text Mining Open Article Page

Text Mining

Process of analysing text to extract information from it Text mining , text data mining ( TDM ) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Critical Symbolic Virtual Narrative Open Text Mining Open Article Page

Exploring foci of: arXiv (Cornell University) Text2Grad: Reinforcement Learning from Natural Language Feedback 2025 Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving interpretability but leaving model parameters untouched. We introduce Text2Grad, a reinforcement-learning paradigm that turns free-form textual feedback into span-level gradients. Given human (or programmatic) critiques, Text2Grad aligns each feedback phrase … Open Article Page

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Explore Text Mining W Topic Text Mining arXiv (Cornell University) Text2Grad: Reinforcement Learning from Natural Language Feedback 2025 Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving… Article Critical Symbolic Virtual Narrative Open Text Mining Open Article Page