1 citations · 3 across the 11 of their papers we have counts for
12 papers · 1 filter
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…
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization
Yasaman Jafari, Dheeraj Mekala, Rose Yu +1
RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions. However, in many target ap…
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…