7 papers · 1 filter
APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
Artem Chernodub, Aman Saini, Yejin Huh +2
Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. C…
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Zae Myung Kim, Chanwoo Park, Vipul Raheja +2
Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reli…
Benchmarking Cognitive Biases in Large Language Models as Evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja +3
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-cont…
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
Zae Myung Kim, Kwang Hee Lee, Preston Zhu +2
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of id…
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
Aman Saini, Artem Chernodub, Vipul Raheja +1
We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-onl…
mEdIT: Multilingual Text Editing via Instruction Tuning
Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni +2
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lin…