172 citations · 646 across the 82 of their papers we have counts for
8 papers · 2 filters
QA-Noun: Representing Nominal Semantics via Natural Language Question-Answer Pairs
Maria Tseytlin, Paul Roit, Omri Abend +2
Decomposing sentences into fine-grained meaning units is increasingly used to model semantic alignment. While QA-based semantic approaches have shown effectiveness for representing…
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…
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…
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…
GenerationPrograms: Fine-grained Attribution with Executable Programs
David Wan, Eran Hirsch, Elias Stengel-Eskin +2
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their out…
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…