84 citations · 274 across the 16 of their papers we have counts for
17 papers
Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling
Yunyao Yu, Zhengxian Wu, Zhuohong Chen +6
In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for stable and effective learning. However,…
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang +19
The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
Lingbo Mo, Shijie Chen, Ziru Chen +10
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking…
Mind2Web: Towards a Generalist Agent for the Web
Xiang Deng, Yu Gu, Boyuan Zheng +5
We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website.…
Exploring Chain-of-Thought Style Prompting for Text-to-SQL
Chang-You Tai, Ziru Chen, Tianshu Zhang +2
In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance o…
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis
Xiang Deng, Vasilisa Bashlovkina, Feng Han +2
Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resu…
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
Boshi Wang, Sewon Min, Xiang Deng +4
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermed…
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
Yu Gu, Xiang Deng, Yu Su
A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generat…
Bootstrapping a User-Centered Task-Oriented Dialogue System
Shijie Chen, Ziru Chen, Xiang Deng +8
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement…
Iteratively Prompt Pre-trained Language Models for Chain of Thought
Boshi Wang, Xiang Deng, Huan Sun
While Pre-trained Language Models (PLMs) internalize a great amount of world knowledge, they have been shown incapable of recalling these knowledge to solve tasks requiring complex…
DOM-LM: Learning Generalizable Representations for HTML Documents
Xiang Deng, Prashant Shiralkar, Colin Lockard +2
HTML documents are an important medium for disseminating information on the Web for human consumption. An HTML document presents information in multiple text formats including unst…
ReasonBERT: Pre-trained to Reason with Distant Supervision
Xiang Deng, Yu Su, Alyssa Lees +3
We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike exist…
Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek +3
Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to grou…
TURL: Table Understanding through Representation Learning
Xiang Deng, Huan Sun, Alyssa Lees +2
Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table und…
Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation
Jie Zhao, Xiang Deng, Huan Sun
This paper makes one of the first efforts toward automatically generating complex questions from knowledge graphs. Particularly, we study how to leverage existing simple question d…
Automatic Table completion using Knowledge Base
Bortik Bandyopadhyay, Xiang Deng, Goonmeet Bajaj +2
Table is a popular data format to organize and present relational information. Users often have to manually compose tables when gathering their desiderate information (e.g., entiti…
Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction
Xiang Deng, Huan Sun
Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentenc…