84 citations · 357 across the 32 of their papers we have counts for
34 papers
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…
Biomedical Language Models are Robust to Sub-optimal Tokenization
Bernal Jiménez Gutiérrez, Huan Sun, Yu Su
As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise.…
MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing
Kai Zhang, Lingbo Mo, Wenhu Chen +2
Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot o…
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.…
Text-to-SQL Error Correction with Language Models of Code
Ziru Chen, Shijie Chen, Michael White +5
Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-…
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
Tianshu Zhang, Changchang Liu, Wei-Han Lee +2
This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing…
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…
Error Detection for Text-to-SQL Semantic Parsing
Shijie Chen, Ziru Chen, Huan Sun +1
Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. Specifically, modern text-to-SQL parsers…
Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate
Boshi Wang, Xiang Yue, Huan Sun
Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive performance in complex reasoning tasks. However, it is difficult to know whether the models are reasoni…
Automatic Evaluation of Attribution by Large Language Models
Xiang Yue, Boshi Wang, Ziru Chen +3
A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. How…
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
Zhen Wang, Rameswar Panda, Leonid Karlinsky +3
Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting lar…
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…
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin A. Inan, Xuechen Li +6
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating syntheti…
Synthetic Question Value Estimation for Domain Adaptation of Question Answering
Xiang Yue, Ziyu Yao, Huan Sun
Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic que…
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington +4
The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which…
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…
TopNet: Learning from Neural Topic Model to Generate Long Stories
Yazheng Yang, Boyuan Pan, Deng Cai +1
Long story generation (LSG) is one of the coveted goals in natural language processing. Different from most text generation tasks, LSG requires to output a long story of rich conte…
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
Lingbo Mo, Ashley Lewis, Huan Sun +1
Existing studies on semantic parsing focus primarily on mapping a natural-language utterance to a corresponding logical form in one turn. However, because natural language can cont…
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…
Differential Privacy for Text Analytics via Natural Text Sanitization
Xiang Yue, Minxin Du, Tianhao Wang +3
Texts convey sophisticated knowledge. However, texts also convey sensitive information. Despite the success of general-purpose language models and domain-specific mechanisms with d…
Learning Structural Edits via Incremental Tree Transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin +2
While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed mode…
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization
Jie Zhao, Huan Sun
Code retrieval is a key task aiming to match natural and programming languages. In this work, we propose adversarial learning for code retrieval, that is regularized by question-de…