26 citations · 56 across the 23 of their papers we have counts for
24 papers
Consensus or Conflict? Fine-Grained Evaluation of Conflicting Answers in Question-Answering
Eviatar Nachshoni, Arie Cattan, Shmuel Amar +2
Large Language Models (LLMs) have demonstrated strong performance in question answering (QA) tasks. However, Multi-Answer Question Answering (MAQA), where a question may have sever…
DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
Arie Cattan, Alon Jacovi, Ori Ram +6
Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources…
CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection
Ron Eliav, Arie Cattan, Eran Hirsch +4
A common approach to hallucination detection casts it as a natural language inference (NLI) task, often using LLMs to classify whether the generated text is entailed by correspondi…
QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization
Shiyue Zhang, David Wan, Arie Cattan +3
How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking th…
Localizing Factual Inconsistencies in Attributable Text Generation
Arie Cattan, Paul Roit, Shiyue Zhang +5
There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existin…
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…
SEAM: A Stochastic Benchmark for Multi-Document Tasks
Gili Lior, Avi Caciularu, Arie Cattan +3
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a…
DoubleDipper: Improving Long-Context LLMs via Context Recycling
Arie Cattan, Alon Jacovi, Alex Fabrikant +8
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel…
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…
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Arie Cattan, Tom Hope, Doug Downey +4
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-docu…
From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
Arie Cattan, Lilach Eden, Yoav Kantor +1
Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of c…
Evaluating and Improving the Coreference Capabilities of Machine Translation Models
Asaf Yehudai, Arie Cattan, Omri Abend +1
Machine translation (MT) requires a wide range of linguistic capabilities, which current end-to-end models are expected to learn implicitly by observing aligned sentences in biling…
F-coref: Fast, Accurate and Easy to Use Coreference Resolution
Shon Otmazgin, Arie Cattan, Yoav Goldberg
We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode…
How "Multi" is Multi-Document Summarization?
Ruben Wolhandler, Arie Cattan, Ori Ernst +1
The task of multi-document summarization (MDS) aims at models that, given multiple documents as input, are able to generate a summary that combines disperse information, originally…
LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
Shon Otmazgin, Arie Cattan, Yoav Goldberg
While coreference resolution typically involves various linguistic challenges, recent models are based on a single pairwise scorer for all types of pairs. We present LingMess, a ne…
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
Eran Hirsch, Alon Eirew, Ori Shapira +7
We introduce iFacetSum, a web application for exploring topical document sets. iFacetSum integrates interactive summarization together with faceted search, by providing a novel fac…
Realistic Evaluation Principles for Cross-document Coreference Resolution
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. W…
Cross-document Coreference Resolution over Predicted Mentions
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challe…
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia
Alon Eirew, Arie Cattan, Ido Dagan
Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and rela…
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
Arie Cattan, Sophie Johnson, Daniel Weld +4
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding. Previous work on cross-document coreference resolutio…
CD2CR: Co-reference Resolution Across Documents and Domains
James Ravenscroft, Arie Cattan, Amanda Clare +2
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models f…
CDLM: Cross-Document Language Modeling
Avi Caciularu, Arman Cohan, Iz Beltagy +3
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First,…
Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation…
CoRefi: A Crowd Sourcing Suite for Coreference Annotation
Aaron Bornstein, Arie Cattan, Ido Dagan
Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and…