1 citations · 1 across the 7 of their papers we have counts for
7 papers
When is the consistent prediction likely to be a correct prediction?
Alex Nguyen, Dheeraj Mekala, Chengyu Dong +1
Self-consistency (Wang et al., 2023) suggests that the most consistent answer obtained through large language models (LLMs) is more likely to be correct. In this paper, we challeng…
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering
Alex Nguyen, Zilong Wang, Jingbo Shang +1
The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists i…
TOOLVERIFIER: Generalization to New Tools via Self-Verification
Dheeraj Mekala, Jason Weston, Jack Lanchantin +4
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learn…
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Dheeraj Mekala, Alex Nguyen, Jingbo Shang
Instruction-tuning language models has become a crucial step in aligning them for general use. Typically, this process involves extensive training on large datasets, incurring high…
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase
Dawei Li, Yaxuan Li, Dheeraj Mekala +5
In-Context Learning (ICL) combined with pre-trained large language models has achieved promising results on various NLP tasks. However, ICL requires high-quality annotated demonstr…
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank
Dheeraj Mekala, Adithya Samavedhi, Chengyu Dong +1
Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection. Traditional sup…