Papers with realistic scenario

5 papers
MUG: Interactive Multimodal Grounding on User Interfaces (2024.findings-eacl)

Copied to clipboard

Challenge: Prior studies modeled multimodal UI grounding in one round, but such an interaction is inherently iterative.
Approach: They propose a task where a user and an agent collaborate on an interface screen . they use a dataset of 77,820 sequences of human user-agent interaction on mobile interfaces .
Outcome: The proposed task improves the absolute task completion by 18% over the entire test set and 31% over the challenging split.
Exploring Backdoor Vulnerabilities of Chat Models (2025.coling-main)

Copied to clipboard

Challenge: Recent studies show that Large Language Models (LLMs) are susceptible to a security threat known as Backdoor Attack.
Approach: They propose a backdoor attack method that distributes trigger scenarios across user inputs in different rounds and makes the backdoor be triggered only when all trigger scenarios have appeared in the historical conversations.
Outcome: The proposed method achieves high attack success rates on chat models while maintaining normal capabilities on providing helpful responses to benign user requests.
Cross-Domain Detection of GPT-2-Generated Technical Text (2022.naacl-main)

Copied to clipboard

Challenge: Recent advances in techniques for generating realistic synthetic content pose a diverse set of problems with significant societal consequences.
Approach: They propose to use paragraph-level detectors to detect tampering of full-length documents under a variety of threat models to detect machine-generated text.
Outcome: The proposed detectors can detect the tampering of full-length documents under a variety of threat models.
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection (2021.findings-emnlp)

Copied to clipboard

Challenge: Existing methods to detect out-of-domain (OOD) inputs are limited and lack data.
Approach: They propose a new architecture that extends Prototypical Networks to process in-domain and OOD sentences via Mutual Information Maximization objective.
Outcome: The proposed method significantly improves performance up to 20% for OOD detection in low resource settings of text classification.
Complex Claim Verification with Evidence Retrieved in the Wild (2024.naacl-long)

Copied to clipboard

Challenge: Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: no access to evidence, access to curated evidence, or access to published evidence after a claim was made.
Approach: They propose a pipeline to check claims using raw evidence from the web . they restrict their retriever to only search documents available prior to the claim's making .
Outcome: The proposed method is based on a political claim dataset and shows that the evidence summary produced by the system is reliable and relevant to answering key questions.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations