Search Results for author: Aviv Slobodkin

Found 14 papers, 7 papers with code

EventFull: Complete and Consistent Event Relation Annotation

1 code implementation17 Dec 2024 Alon Eirew, Eviatar Nachshoni, Aviv Slobodkin, Ido Dagan

Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types.

Relation

GRADE: Quantifying Sample Diversity in Text-to-Image Models

no code implementations29 Oct 2024 Royi Rassin, Aviv Slobodkin, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg

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'').

Attribute Diversity +3

Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models

no code implementations28 Jul 2024 Nitzan Bitton-Guetta, Aviv Slobodkin, Aviya Maimon, Eliya Habba, Royi Rassin, Yonatan Bitton, Idan Szpektor, Amir Globerson, Yuval Elovici

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.

World Knowledge

Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP

no code implementations29 Jun 2024 Omer Goldman, Alon Jacovi, Aviv Slobodkin, Aviya Maimon, Ido Dagan, Reut Tsarfaty

By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area.

All Book summarization +1

The Power of Summary-Source Alignments

1 code implementation2 Jun 2024 Ori Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar, Mohit Bansal, Jacob Goldberger, Ran Levy, Ido Dagan

Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.

Document Summarization Multi-Document Summarization +1

Attribute First, then Generate: Locally-attributable Grounded Text Generation

1 code implementation25 Mar 2024 Aviv Slobodkin, Eran Hirsch, Arie Cattan, Tal Schuster, Ido Dagan

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.

Attribute Document Summarization +5

Multi-Review Fusion-in-Context

no code implementations22 Mar 2024 Aviv Slobodkin, Ori Shapira, Ran Levy, Ido Dagan

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.

Long Form Question Answering Text Generation

SummHelper: Collaborative Human-Computer Summarization

no code implementations16 Aug 2023 Aviv Slobodkin, Niv Nachum, Shmuel Amar, Ori Shapira, Ido Dagan

Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process.

Text Summarization

Controlled Text Reduction

2 code implementations24 Oct 2022 Aviv Slobodkin, Paul Roit, Eran Hirsch, Ori Ernst, Ido Dagan

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.

Mediators in Determining what Processing BERT Performs First

1 code implementation NAACL 2021 Aviv Slobodkin, Leshem Choshen, Omri Abend

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.

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