84 citations · 357 across the 32 of their papers we have counts for
12 papers · 1 filter
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…