18 citations · 36 across the 12 of their papers we have counts for
5 papers · 1 filter
TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge
Cheng-Han Chiang, Hung-yi Lee, Michal Lukasik
The LLM-as-a-judge paradigm uses large language models (LLMs) for automated text evaluation, where a numerical assessment is assigned by an LLM to the input text following scoring…
Regression-aware Inference with LLMs
Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon +2
Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregres…
Large Language Models with Controllable Working Memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer +5
Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), owing to their excellent understanding and generation abilities. Remarkably…
Semantic Label Smoothing for Sequence to Sequence Problems
Michal Lukasik, Himanshu Jain, Aditya Krishna Menon +4
Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such method…
Text Segmentation by Cross Segment Attention
Michal Lukasik, Boris Dadachev, Gonçalo Simões +1
Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as informa…