REVIEW 13 cited by
Evaluating the Factual Consistency of Abstractive Text Summarization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Evaluating the Factual Consistency of Abstractive Text Summarization
read the original abstract
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary. Training data is generated by applying a series of rule-based transformations to the sentences of source documents. The factual consistency model is then trained jointly for three tasks: 1) identify whether sentences remain factually consistent after transformation, 2) extract a span in the source documents to support the consistency prediction, 3) extract a span in the summary sentence that is inconsistent if one exists. Transferring this model to summaries generated by several state-of-the art models reveals that this highly scalable approach substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency.
Forward citations
Cited by 13 Pith papers
-
Layer-Resolved Optimal Transport for Hallucination Detection in NMT and Abstractive Summarization
Layer-resolved OT detects source-disengagement hallucinations in NMT but achieves only 57% balanced accuracy on summarization because content misrepresentation can occur with correct attention.
-
Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts
Diffusion-based localized editing framework for faithful summarization of evolving contexts, introducing the StreamSum benchmark and showing tradeoffs in faithfulness, speed, and preservation.
-
ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop
ProcAgent demonstrates a complete step-by-step assembly assistant that runs on a single edge device by pairing cheap continuous perception with on-demand vision-language verification, a task graph, and human confirmation.
-
Constrained Paraphrase Consistency for LLM Hallucination Detection
CCHD formulates hallucination detector training as constrained optimization with paraphrase-consistency and label-preservation rules solved via gradient descent-ascent, outperforming baselines on factuality benchmarks.
-
Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
-
No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation
NWCAD uses a two-stream setup with a two-stage gate to prevent accuracy drops on baseline-correct items under non-informative contexts while retaining gains from helpful contexts.
-
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
A multi-role red-teaming framework with attacker, target, and jury LLMs measures faithfulness in English and Arabic, finding false-premise prompts and length limits change unfaithfulness rates.
-
Cross Paraphrastic Invariance Learning for Hallucination Detection
CPIL is a contrastive two-stage method that enforces paraphrase invariance on limited labeled data to outperform baselines in hallucination detection across 11 tasks.
-
HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings.
-
A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization
An expert-editor stepwise-questioning multi-agent pipeline improves ROUGE/BERTScore/FactCC for long scientific summarization on two datasets relative to direct generation and HERA.
-
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.
-
HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
HalluScan benchmark evaluates hallucination detection in LLMs, reporting NLI Verification at AUROC 0.88 and introducing HalluScore (r=0.41 with humans) plus Adaptive Detection Routing for 2x cost savings.
-
A Community-Based Approach for Stance Distribution and Argument Organization
Unsupervised graph community detection organizes arguments to reveal stance distributions in debates.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.