Search Results for author:
Found 15 papers, 7 papers with code
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image.
EventFull: Complete and Consistent Event Relation Annotation
Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types.
GRADE: Quantifying Sample Diversity in Text-to-Image Models
GRADE leverages the world knowledge embedded in large language models and visual question-answering systems to identify relevant concept-specific axes of diversity (e. g., ``shape'' and ``color'' for the concept ``cookie'').
Explicating the Implicit: Argument Detection Beyond Sentence Boundaries
Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task.
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models
To study these skills, we present Visual Riddles, a benchmark aimed to test vision and language models on visual riddles requiring commonsense and world knowledge.
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area.
The Power of Summary-Source Alignments
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.
Attribute First, then Generate: Locally-attributable Grounded Text Generation
Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections.
Multi-Review Fusion-in-Context
This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content.
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.
Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans
Further, we substantially improve the silver training data quality via GPT-4 distillation.
SummHelper: Collaborative Human-Computer Summarization
Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process.
Controlled Text Reduction
Producing a reduced version of a source text, as in generic or focused summarization, inherently involves two distinct subtasks: deciding on targeted content and generating a coherent text conveying it.
Semantics-aware Attention Improves Neural Machine Translation
We further show an additional gain when using both semantic and syntactic structures in some language pairs.
Mediators in Determining what Processing BERT Performs First
Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks.
