16 citations · 58 across the 16 of their papers we have counts for
16 papers
Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
Yijia Shao, Humishka Zope, Yucheng Jiang +4
The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on autom…
Challenges and Paths Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li +7
AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be ad…
Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration
Yijia Shao, Vinay Samuel, Yucheng Jiang +2
While the advancement of large language models has spurred the development of AI agents to automate tasks, numerous use cases inherently require agents to collaborate with humans d…
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 via Likelihood Ratio Based Task Prediction
Haowei Lin, Yijia Shao, Weinan Qian +3
Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key…
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…
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks
Xinyi He, Mengyu Zhou, Mingjie Zhou +7
Tabular data analysis is performed every day across various domains. It requires an accurate understanding of field semantics to correctly operate on table fields and find common p…