16 citations · 58 across the 16 of their papers we have counts for
11 papers · 1 filter
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
Yijia Shao, Tianshi Li, Weiyan Shi +2
As language models (LMs) are widely utilized in personalized communication scenarios (e.g., sending emails, writing social media posts) and endowed with a certain level of agency,…
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
Yucheng Jiang, Yijia Shao, Dekun Ma +2
While language model (LM)-powered chatbots and generative search engines excel at answering concrete queries, discovering information in the terrain of unknown unknowns remains cha…
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Eric Zelikman, Georges Harik, Yijia Shao +3
When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tas…
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
Yijia Shao, Yucheng Jiang, Theodore A. Kanell +3
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplore…
Class-Incremental Learning based on Label Generation
Yijia Shao, Yiduo Guo, Dongyan Zhao +1
Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) se…
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems
Sarik Ghazarian, Yijia Shao, Rujun Han +2
Commonsense reasoning is omnipresent in human communications and thus is an important feature for open-domain dialogue systems. However, evaluating commonsense in dialogue systems…
Continual Pre-training of Language Models
Zixuan Ke, Yijia Shao, Haowei Lin +3
Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain…
Adapting a Language Model While Preserving its General Knowledge
Zixuan Ke, Yijia Shao, Haowei Lin +3
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a…
LUNA: Language Understanding with Number Augmentations on Transformers via Number Plugins and Pre-training
Hongwei Han, Jialiang Xu, Mengyu Zhou +3
Transformers are widely used in NLP tasks. However, current approaches to leveraging transformers to understand language expose one weak spot: Number understanding. In some scenari…
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
Yijia Shao, Mengyu Zhou, Yifan Zhong +5
Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where que…
Continual Training of Language Models for Few-Shot Learning
Zixuan Ke, Haowei Lin, Yijia Shao +3
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can pr…