mlsanityData to 7 Oct 2026

MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection

arXiv 2405.19285 ↗First posted 29 May 2024cs.CLcited by 4

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Abstract (arXiv)

Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

22 of 46 references.

References · 22

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  4. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  5. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
  6. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 409
  7. 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
  8. 2021Measuring Attribution in Natural Language Generation Modelscited by 253
  9. 2020Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasetscited by 137
  10. 2020GO FIGURE: A Meta Evaluation of Factuality in Summarizationcited by 75
  11. 2020Leveraging Abstract Meaning Representation for Knowledge Base Question Answeringcited by 53
  12. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
10 more
  1. 2022QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakerscited by 39
  2. 2022Modern Baselines for SPARQL Semantic Parsingcited by 34
  3. 2021Maximum Bayes Smatch Ensemble Distillation for AMR Parsingcited by 27
  4. 2020AMR Similarity Metrics from Principlescited by 21
  5. 2022A Survey for Efficient Open Domain Question Answeringcited by 20
  6. 2023SMATCH++: Standardized and Extended Evaluation of Semantic Graphscited by 19
  7. 2018Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehensioncited by 17
  8. 2023"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representationcited by 15
  9. 2022Inducing and Using Alignments for Transition-based AMR Parsingcited by 11
  10. 2021Learning to Transpile AMR into SPARQLcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Modelscited by 7
  2. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  3. 2025HuAMR: A Hungarian AMR Parser and Datasetcited by 0
  4. 2026SWAN: Semantic Watermarking with Abstract Meaning Representationcited by 0