Abstract There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existing methods fail to precisely pinpoint the errors. In this work, we introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation, at a fine-grained level. Drawing inspiration from Neo-Davidsonian formal semantics, we propose decomposing the generated text into minimal predicate-argument level propositions, expressed as simple question-answer (QA) pairs, and assess whether each individual QA pair is supported by a trusted reference text. As each QA pair corresponds to a single semantic relation between a predicate and an argument, QASemConsistency effectively localizes the unsupported information. We first demonstrate the effectiveness of the QASemConsistency methodology for human annotation, by collecting crowdsourced annotations of granular consistency errors, while achieving a substantial inter-annotator agreement. This benchmark includes more than 3K instances spanning various tasks of attributable text generation. We also show that QASemConsistency yields factual consistency scores that correlate well with human judgments. Finally, we implement several methods for automatically detecting localized factual inconsistencies, with both supervised entailment models and LLMs.1

Paul Roit, Aviv Slobodkin, Eran Hirsch, Arie Cattan, Ayal Klein, Valentina Pyatkin, Ido Dagan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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.Yet, these citations often point to entire documents or paragraphs, burdening users with extensive verification work.In this paper, we introduce a locally-attributable text generation approach, prioritizing concise attributions.Our method, named "Attribute First, then Generate", breaks down the conventional end-to-end generation process into three intuitive steps: content selection, sentence planning, and sequential sentence generation.By initially identifying relevant source segments ("select first") and then conditioning the generation process on them ("then generate"), we ensure these segments also act as the output's fine-grained attributions ("select" becomes "attribute").Tested on Multi-document Summarization and Long-form Question-answering, our method not only yields more concise citations than the baselines but also maintains-and in some cases enhances-both generation quality and attribution accuracy.Furthermore, it significantly reduces the time required for fact verification by human assessors.

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 concise sentences or phrases, termed key points, and quantifies their prevalence.While key points are more expressive than word clouds and key phrases, making sense of a long, flat list of key points, which often express related ideas in varying levels of granularity, may still be challenging.To address this limitation of KPA, we introduce the task of organizing a given set of key points into a hierarchy, according to their specificity.Such hierarchies may be viewed as a novel type of Textual Entailment Graph.We develop THINKP, a high quality benchmark dataset of key point hierarchies for business and product reviews, obtained by consolidating multiple annotations.We compare different methods for predicting pairwise relations between key points, and for inferring a hierarchy from these pairwise predictions.In particular, for the task of computing pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision.

Current state-of-the-art coreference systems are based on a single pairwise scoring component, which assigns to each pair of mention spans a score reflecting their tendency to corefer to each other.We observe that different kinds of mention pairs require different information sources to assess their score.We present LINGMESS, a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category.This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus and 5 additional datasets.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 bilingual corpora.In this work, we ask: How well do MT models learn coreference resolution from implicit signal?To answer this question, we develop an evaluation methodology that derives coreference clusters from MT output and evaluates them without requiring annotations in the target language.We further evaluate several prominent open-source and commercial MT systems, translating from English to six target languages, and compare them to state-of-theart coreference resolvers on three challenging benchmarks.Our results show that the monolingual resolvers greatly outperform MT models.Motivated by this result, we experiment with different methods for incorporating the output of coreference resolution models in MT, showing improvement over strong baselines.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 spread across these documents.Accordingly, it is expected that both reference summaries in MDS datasets, as well as system summaries, would indeed be based on such dispersed information.In this paper, we argue for quantifying and assessing this expectation.To that end, we propose an automated measure for evaluating the degree to which a summary is "disperse", in the sense of the number of source documents needed to cover its content.We apply our measure to empirically analyze several popular MDS datasets, with respect to their reference summaries, as well as the output of state-of-the-art systems.Our results show that certain MDS datasets barely require combining information from multiple documents, where a single document often covers the full summary content.Overall, we advocate using our metric for assessing and improving the degree to which summarization datasets require combining multi-document information, and similarly how summarization models actually meet this challenge.1

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 for this task assume that all documents are of the same type (e.g.news articles) or fall under the same theme.However, it is also desirable to perform CDCR across different domains (type or theme).A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them.Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media.We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD 2 CR).The task aims to identify links between entities across heterogeneous document types.We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD 2 CR.Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.

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 relatively small, while annotating only modestsize clusters of documents belonging to the same topic.To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a largescale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics.We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset.Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages.To set baseline results, we develop an algorithm that adapts components of stateof-the-art models for within-document coreference resolution to the cross-document setting.Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.