Search Results for author:
Found 5 papers, 3 papers with code
Reinforcement Learning with Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation
Style is an integral component of text that expresses a diverse set of information, including interpersonal dynamics (e. g. formality) and the author's emotions or attitudes (e. g. disgust).
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
This work delves into the expanding role of large language models (LLMs) in generating artificial data.
Benchmarking Cognitive Biases in Large Language Models as Evaluators
According to our findings, LLMs may still be unable to be utilized for automatic annotation aligned with human preferences.
CoEdIT: Text Editing by Task-Specific Instruction Tuning
We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).
Decoding the End-to-end Writing Trajectory in Scholarly Manuscripts
We also provide ManuScript, an original dataset annotated with a simplified version of our taxonomy to show writer actions and the intentions behind them.
