14 citations · 31 across the 20 of their papers we have counts for
17 papers · 1 filter
Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma
Xuechen Zhang, Aviv Slobodkin, Joydeep Paul +4
Representation learning for geospatial and spatio-temporal data plays a critical role in enabling general-purpose geospatial intelligence. Recent geospatial foundation models, such…
Effective QA-driven Annotation of Predicate-Argument Relations Across Languages
Jonathan Davidov, Aviv Slobodkin, Shmuel Tomi Klein +3
Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining suc…
PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise
Sapir Harary, Eran Hirsch, Aviv Slobodkin +3
Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether th…
A Unifying Scheme for Extractive Content Selection Tasks
Shmuel Amar, Ori Shapira, Aviv Slobodkin +1
A broad range of NLP tasks involve selecting relevant text spans from given source texts. Despite this shared objective, such \textit{content selection} tasks have traditionally be…
LAQuer: Localized Attribution Queries in Content-grounded Generation
Eran Hirsch, Aviv Slobodkin, David Wan +3
Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy. Existing attribution methods associa…
EventFull: Complete and Consistent Event Relation Annotation
Alon Eirew, Eviatar Nachshoni, Aviv Slobodkin +1
Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types. How…
Explicating the Implicit: Argument Detection Beyond Sentence Boundaries
Paul Roit, Aviv Slobodkin, Eran Hirsch +4
Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task. The typical reader, however, perfectly interprets predicate-argument rela…
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Omer Goldman, Alon Jacovi, Aviv Slobodkin +3
Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. Howeve…
The Power of Summary-Source Alignments
Ori Ernst, Ori Shapira, Aviv Slobodkin +5
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation. In this context, alignment…
Multi-Review Fusion-in-Context
Aviv Slobodkin, Ori Shapira, Ran Levy +1
Grounded text generation, encompassing tasks such as long-form question-answering and summarization, necessitates both content selection and content consolidation. Current end-to-e…
Attribute First, then Generate: Locally-attributable Grounded Text Generation
Aviv Slobodkin, Eran Hirsch, Arie Cattan +2
Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting…
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
Aviv Slobodkin, Omer Goldman, Avi Caciularu +2
Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue aris…
Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans
Aviv Slobodkin, Avi Caciularu, Eran Hirsch +1
The recently introduced Controlled Text Reduction (CTR) task isolates the text generation step within typical summarization-style tasks. It does so by challenging models to generat…
SummHelper: Collaborative Human-Computer Summarization
Aviv Slobodkin, Niv Nachum, Shmuel Amar +2
Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process. In this paper, we introduce Su…
Controlled Text Reduction
Aviv Slobodkin, Paul Roit, Eran Hirsch +2
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 coher…
Semantics-aware Attention Improves Neural Machine Translation
Aviv Slobodkin, Leshem Choshen, Omri Abend
The integration of syntactic structures into Transformer machine translation has shown positive results, but to our knowledge, no work has attempted to do so with semantic structur…
Mediators in Determining what Processing BERT Performs First
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…