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20212026
most citedMagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing

30 citations · 36 across the 12 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

AECP: Artifact-Exclusive Communication Protocol for Multi-Agent Code Generation

Jiaqi Xue, Yanjun Wang, Xiangci Li +5

As AI agents increasingly tackle complex repository-level coding tasks, distributing work across multiple agents is a natural way to scale beyond the capabilities of a single agent…

cs.CL2024

HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals

Lingbo Mo, Shun Jiang, Akash Maharaj +2

Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling i…

cs.CL2024

A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models

Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier +1

Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention…

cs.CL2024

A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents

Lingbo Mo, Zeyi Liao, Boyuan Zheng +3

Language agents powered by large language models (LLMs) have seen exploding development. Their capability of using language as a vehicle for thought and communication lends an incr…

cs.CL2024

Controllable Decontextualization of Yes/No Question and Answers into Factual Statements

Lingbo Mo, Besnik Fetahu, Oleg Rokhlenko +1

Yes/No or polar questions represent one of the main linguistic question categories. They consist of a main interrogative clause, for which the answer is binary (assertion or negati…

cs.CL2023★ 1 cited

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities

Lingbo Mo, Boshi Wang, Muhao Chen +1

The rapid progress in open-source Large Language Models (LLMs) is significantly driving AI development forward. However, there is still a limited understanding of their trustworthi…

cs.CL2023

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…

cs.CL2022★ 1 cited

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…

cs.CL2021★ 2 cited

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…