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Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective

T0 review · 1 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper argues that knowledge graphs can ground large language models against hallucinations if the graphs themselves are trustworthy and evaluation becomes fine-grained.

desk verdict A competent, useful survey on KG-based hallucination mitigation that deserves a serious referee, despite a thin treatment of its own load-bearing assumption about KG correctness. read the letter →

arxiv 2411.14258 v1 pith:7CSL7IWF submitted 2024-11-21 cs.CL cs.AI

Ernests Lavrinovics , Russa Biswas , Johannes Bjerva , Katja Hose This is my paper · ORCID
classification cs.CLcs.AI
keywords LLMFactualityKnowledgeGraphsHallucinationshallucinationdetectionintegrationevaluationmultilingual
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that knowledge graphs, which store facts as entity–relation–entity triples, are the most promising external source for grounding large language models and reducing their tendency to produce plausible but false statements. It surveys current hallucination benchmarks, detection methods, and knowledge-integration techniques, organizing them by the stage at which graph knowledge enters the model: pretraining, inference via prompting, decoding, or adapters, and post-generation repair. The authors conclude that today's resources and evaluations are not yet sufficient: most benchmarks are English-only, score whole responses instead of pinpointing the false span, and provide no graph triples as context. They therefore call for span-level hallucination detection, knowledge integration that does not depend on fragile textual prompts, and multiprompt, multilingual, multitask evaluation. If these directions are followed, the paper's claim is that knowledge-graph grounding can become a reliable, interpretable component of LLM systems.

What carries the argument

The organizing mechanism is a three-stage categorization of where external knowledge enters an LLM system: pretraining, where triples are fused into the training signal; inference, which covers prompting, context-aware decoding, and adapter modules that inject graph information in latent space; and post-generation, where claims extracted from the output are checked against a knowledge graph and patched. A second recurring mechanism is the atomic-claim graph: representing a generated text as a set of triples or atomic facts so that each claim can be entailment-checked against the graph, making both detection and correction interpretable.

What would settle it

Inject deliberate errors into a knowledge graph used to condition an LLM, then ask questions whose correct answers conflict with the injected triples; if the system reliably adopts the wrong facts, the central premise that graph grounding reduces hallucination holds only when the graph itself is flawless. A cleaner version would compare graph-grounded output on a QA benchmark against the same output grounded on graphs with 5%, 10%, and 20% of triples randomly corrupted: if factuality drops roughly in proportion to corruption, graph quality, not grounding architecture, is the binding constraint.

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Extended reading notes

Core claim

The central claim is a structured diagnosis: hallucination mitigation through knowledge graphs is feasible in principle, but the field's current methods and benchmarks are mismatched to the problem. Hallucinations are often subtle and located in a single word or phrase, yet most datasets annotate at response level; most knowledge injection is done by prompting, which is sensitive to template wording and limited by context windows; and nearly all evaluation is monolingual English with a single prompt per question. The paper's own contribution is the categorization of integration approaches and the argument that fine-grained detection—identifying exact hallucinatory spans, as in FELM and MuShroom-2025—is the necessary foundation, because mitigation cannot be reliably measured or targeted without it.

Load-bearing premise

The entire mitigation agenda assumes the knowledge graph's triples are factually correct with respect to the user query; if the graph is incomplete, stale, or wrong, grounding the LLM on it simply replaces one kind of falsehood with another, and the paper provides no mechanism to guarantee graph quality.

Editorial extensions

If this is right

  • Fine-grained, span-level hallucination detection is a prerequisite for reliable mitigation, because response-level scores cannot locate the false part of an output.
  • Knowledge integration should move away from textual prompting toward parametric and architectural methods, since prompts are fragile, context-window-bound, and hard to control.
  • Evaluation must cover multiple prompts, languages, and tasks; single-prompt English results do not demonstrate robustness or generalizability.
  • Large-scale datasets that include accurate knowledge-graph triples as context, with train, development, and test splits, are needed to support parametric knowledge integration and entity extraction.
  • Mixing and matching fundamentally different mitigation methods, such as uncertainty-based detection and knowledge-graph-based detection, should be studied to see how they complement one another.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's own premise implies an inverse hazard the authors do not develop: if the knowledge graph is stale or wrong, grounding will faithfully propagate graph errors, so graph-quality control is as important as the grounding mechanism itself.
  • FactScore-style atomic-fact extraction could be repurposed as a unified graph-based metric: split output into atomic facts, align each to graph triples, and report the fraction supported, giving a standard and interpretable hallucination measure.
  • A testable extension of the multilingual argument is that systems integrating multilingual knowledge graphs should show smaller cross-lingual factual consistency gaps; measuring output consistency across languages for the same question would directly test this.
  • For practitioners, the emphasis on non-prompt integration suggests comparing total cost of ownership: prompt-based retrieval is cheap per query but fragile, while adapter or retrofitting pipelines add inference passes whose factuality gains need to be quantified against their cost.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 4 minor

Summary. This paper is a position survey arguing that Knowledge Graphs (KGs) are a promising route to mitigate hallucinations in Large Language Models (LLMs). It reviews hallucination evaluation datasets and benchmarks, hallucination detection methods, and methods for integrating KG knowledge during pretraining, inference, and post-generation. It then proposes three main research directions: fine-grained hallucination detection, KG integration that moves away from textual prompting, and multiprompt, multilingual, multitask evaluation of factuality. The paper's contribution is a qualitative categorization of KG-based mitigation approaches and a consolidated table of evaluation resources, rather than new experiments or formal derivations.

Significance. If the central argument is accepted, the paper provides a useful agenda for the NLP and semantic-web communities, particularly in its calls for fine-grained hallucination detection, submodule-level error reporting, and multilingual evaluation. The taxonomy in Figure 2 and the resource consolidation in Table 1 are convenient entry points for researchers. The paper is appropriately hedged in several places, acknowledges that hallucination will not be fully eliminated, and identifies genuinely underexplored problems. However, the entire mitigation agenda rests on an explicit but unexamined assumption about KG correctness, which limits the strength of the recommendations as currently stated.

major comments (1)
  1. [Section 3] The load-bearing premise of the paper is stated in Section 3: 'KGs are useful under the assumption that the knowledge graph triples are factually correct with respect to the user query.' Since every mitigation method surveyed in Section 5 conditions on KG content, the paper's agenda presupposes that the external KG is more reliable than the LLM it is meant to correct. The paper nevertheless does not discuss how to ensure or measure that correctness, how to handle KG incompleteness or staleness, or how to resolve conflicts between KG triples and the LLM's internal knowledge. The brief acknowledgment of Lageweg and Kruit's open questions about incomplete or missing KG data in Section 5, and the call for 'accurate KG triples' in Section 7, do not fill this gap because 'accurate' is never defined and no verification mechanism is proposed. I recommend that the paper either add a dedicated research direction on KG quality assurance, provenance, and conflict resolution, or explicitly relax the correctness assumption and explain which mitigation methods remain effective under imperfect KGs.
minor comments (4)
  1. [Section 2 and Section 5] The number of paraphrases in the DefAn dataset is reported inconsistently: Section 2 says each question is accompanied by '15 different paraphrasings,' while Section 5 says 'ten different rephrasings of a question.' Since the paper identifies DefAn as the only dataset supporting multiprompt evaluation, the correct count should be verified and used consistently.
  2. [Section 2 and Table 1] The text states that all datasets in Table 1 except SemEval2025-MuShroom are available only in English, but the table's last note only specifies MuShroom's language count. It would improve clarity to state explicitly which rows were checked for multilingual availability, and to acknowledge that languages other than English may exist in the underlying raw sources even if not distributed as annotated splits.
  3. [Section 5 and Figure 2] Figure 2 includes context-aware decoding under 'Architectural Decoding' and the caption notes that decoding does not explicitly use KGs. The corresponding discussion in Section 5 should carry the same caveat so that readers do not mistake this method as a KG-based integration approach.
  4. [Section 6] The phrase 'in a similar spirite as Li et al. (2023)' contains a typo ('spirite' should be 'spirit'), and the sentence is ambiguous about whether the human-evaluation protocol is from Li et al. or merely analogous to it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a position survey whose claims are argued from cited literature, with only minor background self-citations that are not load-bearing.

full rationale

The paper makes no formal derivation and contains no equations, fitted parameters, or predictions that reduce to its inputs. Its central claim—that KGs are a promising route to mitigate LLM hallucinations—is presented as a research position and is supported by a broad set of independent surveys and benchmark resources (e.g., Augenstein et al. 2024, Huang et al. 2023, Pan et al. 2024, Mizrahi et al. 2024). The two self-citations (Pan et al. 2023 and Kaffee et al. 2023, both including author R. Biswas) are used only as background evidence for existing research directions and are corroborated by other cited work in the same passages; no conclusion is forced through these citations. The explicit assumption in Section 3 that 'KGs are useful under the assumption that the knowledge graph triples are factually correct with respect to the user query' is a substantive limitation and a correctness risk, not a circular step: the paper does not define KG utility in terms of its own conclusions, and it openly notes gaps such as incomplete KG data in Section 5. The taxonomy in Figure 2 and the list of research directions in Section 7 are organizational and positional contributions rather than derived results, so there is no self-definitional or fitted-input circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters and no new entities. Its claims rest on assumptions about KG quality, LLM grounding ability, the tractability of fine-grained detection, and the representativeness of the benchmark selection. These are domain assumptions stated or implied in the text.

assumptions (4)
  • domain assumption KGs are useful for hallucination mitigation only if the KG triples are factually correct with respect to the user query.
    Section 3 states this assumption explicitly. If KGs are incomplete or wrong, conditioning on them can propagate errors.
  • domain assumption LLMs can effectively use provided KG triples to ground their output.
    Section 5 notes that prompt-based integration is fragile and context-window-limited, so effectiveness of grounding is not guaranteed and is itself an open problem.
  • domain assumption Fine-grained hallucination detection can be solved at scale and would provide a stable foundation for mitigation.
    Section 1 asserts this as motivation, but the paper does not demonstrate feasibility.
  • domain assumption The benchmarks listed in Table 1 are representative enough to justify the paper's claims about gaps.
    The table is selective rather than systematic, and the paper does not describe a search protocol.

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Cite this review

Pith. "Pith review of Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective." pith.science (2026). https://pith.science/paper/7CSL7IWF

@misc{pith2026241114258,
  author       = {Pith},
  title        = {Pith review of: Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7CSL7IWF}},
  note         = {Machine review of arXiv:2411.14258}
}
read the original abstract

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This undermines trust and limits the applicability of LLMs in different domains. Knowledge Graphs (KGs), on the other hand, provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges). In recent research, KGs have been leveraged to provide context that can fill gaps in an LLM understanding of certain topics offering a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy while benefiting from their wide applicability. Nonetheless, it is still a very active area of research with various unresolved open problems. In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations. In our discussion, we consider the current use of KGs in LLM systems and identify future directions within each of these challenges.

Figures

Figures reproduced from arXiv: 2411.14258 by the authors.

Figure 1
Figure 1. Example of different types of hallucinations occurring in the same output (Zhang et al., 2023). knowledge for factual grounding that LLMs can be synergized with and conditioned on to improve general factual consistency of an LLM’s output. KGs are structured representations of knowledge in a graph-like structure consisting of entities, rela￾tionships, and attributes that encode factual information about real-world ob… view at source ↗
Figure 2
Figure 2. Our categorization of different stages at which external knowledge can be integrated in an LLM to mitigate hallucinations. *Decoding does not explicitly use KGs although it can be used to prioritize in-context knowledge (such as KG metadata). Name Domain Task types Splits Sub-tasks (n) Size Ext.Knowledge Evaluation Granularity MedHalt Pal et al. (2023) Medical Hallc. Evaluation (Reasoning, IR) Train/Val/Test 7 19k N… view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HyDRA: A Hybrid-Driven Reasoning Architecture for Verifiable Knowledge Graphs

    cs.LG 2025-07 conditional novelty 5.0 of 10

    HyDRA, a contract-driven LLM pipeline for building ontologies and knowledge graphs, scored 42-62% accuracy on MedExQA biomedical QA while an ontology-free baseline scored 95-98%.

Reference graph

Works this paper leans on

52 extracted references · 22 canonical work pages · cited by 1 Pith paper

  1. [1]

    G:)PYmPY;5) 2VY ї ?f - | 12 &

    + is cited as + ESG96 +. In connection with cross-referencing and possible future hyperlinking it is not a good idea to collect more that one literature item in one + +. The so-called Harvard or author-year style of referencing is enabled by the package natbib . With this package the literature can be cited as follows: enumerate [ ] Parenthetical: + WB96 ...

    work page 1996

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  3. [3]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  4. [4]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  5. [5]

    Leslie Lamport, : a document preparation system, Addison Wesley, Massachusetts, 2nd edition, 1994

    1994

  6. [6]

    Augenstein , author T

    author I. Augenstein , author T. Baldwin , author M. Cha , author T. Chakraborty , author G. L. Ciampaglia , author D. Corney , author R. DiResta , author E. Ferrara , author S. Hale , author A. Halevy , et al., title Factuality challenges in the era of large language models and opportunities for fact-checking , journal Nature Machine Intelligence ( year ...

    work page 2024

  7. [7]

    Puccetti , author A

    author G. Puccetti , author A. Rogers , author C. Alzetta , author F. Dell ' Orletta , author A. Esuli , title AI ` news ' content farms are easy to make and hard to detect: A case study in I talian , in: editor L.-W. Ku , editor A. Martins , editor V. Srikumar (Eds.), booktitle Proceedings of the 62nd Annual Meeting of the Association for Computational L...

    2024

  8. [8]

    Huang , author W

    author L. Huang , author W. Yu , author W. Ma , author W. Zhong , author Z. Feng , author H. Wang , author Q. Chen , author W. Peng , author X. Feng , author B. Qin , et al., title A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions , journal arXiv preprint arXiv:2311.05232 ( year 2023 )

    arXiv 2023

Show all 52 references
  1. [9]

    Zhang , author Y

    author Y. Zhang , author Y. Li , author L. Cui , author D. Cai , author L. Liu , author T. Fu , author X. Huang , author E. Zhao , author Y. Zhang , author Y. Chen , et al., title Siren's song in the ai ocean: a survey on hallucination in large language models , journal arXiv ...

    2023 arXiv

  2. [10]

    Perković , author A

    author G. Perković , author A. Drobnjak , author I. Botički , title Hallucinations in llms: Understanding and addressing challenges , in: booktitle 2024 47th MIPRO ICT and Electronics Convention (MIPRO) , year 2024 , pp. pages 2084--2088 . :10.1109/MIPRO60963.2024.10569238

    2024

  3. [11]

    Pan , author L

    author S. Pan , author L. Luo , author Y. Wang , author C. Chen , author J. Wang , author X. Wu , title Unifying large language models and knowledge graphs: A roadmap , journal IEEE Transactions on Knowledge and Data Engineering ( year 2024 )

    2024

  4. [12]

    author J. Z. Pan , author S. Razniewski , author J.-C. Kalo , author S. Singhania , author J. Chen , author S. Dietze , author H. Jabeen , author J. Omeliyanenko , author W. Zhang , author M. Lissandrini , author R. Biswas , author G. de Melo , author A. Bonifati , author E. V...

    2023

  5. [13]

    Sun , author S

    author Y. Sun , author S. Wang , author S. Feng , author S. Ding , author C. Pang , author J. Shang , author J. Liu , author X. Chen , author Y. Zhao , author Y. Lu , et al., title Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation...

    2021 arXiv

  6. [14]

    Hou , author W

    author Y. Hou , author W. Jiao , author M. Liu , author C. Allen , author Z. Tu , author M. Sachan , title Adapters for enhanced modeling of multilingual knowledge and text , in: editor Y. Goldberg , editor Z. Kozareva , editor Y. Zhang (Eds.), booktitle Findings of the Associ...

    2022

  7. [15]

    Li , author X

    author J. Li , author X. Li , author L. Hu , author Y. Zhang , author J. Wang , title Knowledge graph enhanced language models for sentiment analysis , in: booktitle International Semantic Web Conference , organization Springer , year 2023 , pp. pages 447--464

    2023

  8. [16]

    Shi , author X

    author W. Shi , author X. Han , author M. Lewis , author Y. Tsvetkov , author L. Zettlemoyer , author W.-t. Yih , title Trusting your evidence: Hallucinate less with context-aware decoding , in: editor K. Duh , editor H. Gomez , editor S. Bethard (Eds.), booktitle Proceedings ...

    2024

  9. [17]

    Chuang , author Y

    author Y.-S. Chuang , author Y. Xie , author H. Luo , author Y. Kim , author J. Glass , author P. He , title Dola: Decoding by contrasting layers improves factuality in large language models , journal arXiv preprint arXiv:2309.03883 ( year 2023 )

    2023 arXiv

  10. [18]

    Tian , author Y

    author S. Tian , author Y. Luo , author T. Xu , author C. Yuan , author H. Jiang , author C. Wei , author X. Wang , title KG -adapter: Enabling knowledge graph integration in large language models through parameter-efficient fine-tuning , in: booktitle Findings of the Associat...

    2024 doi

  11. [19]

    author L. F. R. Ribeiro , author M. Liu , author I. Gurevych , author M. Dreyer , author M. Bansal , title F act G raph: Evaluating factuality in summarization with semantic graph representations , in: booktitle Proceedings of the 2022 Conference of the North American Chapter ...

    2022

  12. [20]

    Sansford , author N

    author H. Sansford , author N. Richardson , author H. P. Maretic , author J. N. Saada , title Grapheval: A knowledge-graph based llm hallucination evaluation framework , journal arXiv preprint arXiv:2407.10793 ( year 2024 )

    2024 arXiv

  13. [21]

    Luo , author Y.-F

    author L. Luo , author Y.-F. Li , author G. Haffari , author S. Pan , title Reasoning on graphs: Faithful and interpretable large language model reasoning , in: booktitle International Conference on Learning Representations , year 2024

    2024

  14. [22]

    Sun , author C

    author J. Sun , author C. Xu , author L. Tang , author S. Wang , author C. Lin , author Y. Gong , author H.-Y. Shum , author J. Guo , title Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph , journal arXiv preprint arXiv:2307.07697 ( y...

    2023 arXiv

  15. [23]

    Martino , author M

    author A. Martino , author M. Iannelli , author C. Truong , title Knowledge injection to counter large language model (llm) hallucination , in: booktitle European Semantic Web Conference , organization Springer , year 2023 , pp. pages 182--185

    2023

  16. [24]

    Guan , author Y

    author X. Guan , author Y. Liu , author H. Lin , author Y. Lu , author B. He , author X. Han , author L. Sun , title Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting , in: booktitle Proceedings of the AAAI Conference on Artificia...

    2024

  17. [25]

    Zhang* , author V

    author T. Zhang* , author V. Kishore* , author F. Wu* , author K. Q. Weinberger , author Y. Artzi , title Bertscore: Evaluating text generation with bert , in: booktitle International Conference on Learning Representations , year 2020 . https://openreview.net/forum?id=SkeHuCVFDr

    2020

  18. [26]

    Yuan , author G

    author W. Yuan , author G. Neubig , author P. Liu , title Bartscore: Evaluating generated text as text generation , journal Advances in Neural Information Processing Systems volume 34 ( year 2021 ) pages 27263--27277

    2021

  19. [27]

    Zhao , author J

    author Y. Zhao , author J. Zhang , author I. Chern , author S. Gao , author P. Liu , author J. He , et al., title Felm: Benchmarking factuality evaluation of large language models , journal Advances in Neural Information Processing Systems volume 36 ( year 2024 )

    2024

  20. [28]

    V\'azquez , author T

    author R. V\'azquez , author T. Mickus , author E. Zosa , author T. Vahtola , author J. Tiedemann , author A. Sinha , author V. Segonne , author F. S\'anchez-Vega , author A. Raganato , author J. Karlgren , author S. Ji , author L. Guillou , author J. Attieh , author M. Apidia...

    2025

  21. [29]

    Mizrahi , author G

    author M. Mizrahi , author G. Kaplan , author D. Malkin , author R. Dror , author D. Shahaf , author G. Stanovsky , title State of what art? a call for multi-prompt llm evaluation , journal Transactions of the Association for Computational Linguistics volume 12 ( year 2024 ) p...

    2024

  22. [30]

    Rahman , author S

    author A. Rahman , author S. Anwar , author M. Usman , author A. Mian , title Defan: Definitive answer dataset for llms hallucination evaluation , journal arXiv preprint arXiv:2406.09155 ( year 2024 )

    2024 arXiv

  23. [31]

    Pal , author L

    author A. Pal , author L. K. Umapathi , author M. Sankarasubbu , title M ed- HALT : Medical domain hallucination test for large language models , in: editor J. Jiang , editor D. Reitter , editor S. Deng (Eds.), booktitle Proceedings of the 27th Conference on Computational Natu...

    2023 doi

  24. [32]

    Li , author X

    author J. Li , author X. Cheng , author X. Zhao , author J.-Y. Nie , author J.-R. Wen , title Halueval: A large-scale hallucination evaluation benchmark for large language models , in: booktitle The 2023 Conference on Empirical Methods in Natural Language Processing , year 2023

    2023

  25. [33]

    Mickus , author E

    author T. Mickus , author E. Zosa , author R. Vazquez , author T. Vahtola , author J. Tiedemann , author V. Segonne , author A. Raganato , author M. Apidianaki , title S em E val-2024 task 6: SHROOM , a shared-task on hallucinations and related observable overgeneration mistak...

    2024

  26. [34]

    Lin , author J

    author S. Lin , author J. Hilton , author O. Evans , title T ruthful QA : Measuring how models mimic human falsehoods , in: editor S. Muresan , editor P. Nakov , editor A. Villavicencio (Eds.), booktitle Proceedings of the 60th Annual Meeting of the Association for Computation...

    2022 doi

  27. [35]

    author S. S. Ravi , author B. Mielczarek , author A. Kannappan , author D. Kiela , author R. Qian , title Lynx: An open source hallucination evaluation model , year 2024 . https://arxiv.org/abs/2407.08488. http://arxiv.org/abs/2407.08488 arXiv:2407.08488

    2024 arXiv

  28. [36]

    https://cdn.openai.com/papers/simpleqa.pdf

    author OpenAI , title Measuring short-form factuality in large language models , year 2024 . https://cdn.openai.com/papers/simpleqa.pdf

    2024

  29. [37]

    Xu , author S

    author Z. Xu , author S. Jain , author M. Kankanhalli , title Hallucination is inevitable: An innate limitation of large language models , journal arXiv preprint arXiv:2401.11817 ( year 2024 )

    2024 arXiv

  30. [38]

    Banerjee , author A

    author S. Banerjee , author A. Agarwal , author S. Singla , title Llms will always hallucinate, and we need to live with this , journal arXiv preprint arXiv:2409.05746 ( year 2024 )

    2024 arXiv

  31. [39]

    Rashad , author A

    author M. Rashad , author A. Zahran , author A. Amin , author A. Abdelaal , author M. Altantawy , title F act A lign: Fact-level hallucination detection and classification through knowledge graph alignment , in: booktitle Proceedings of the 4th Workshop on Trustworthy Natural ...

    2024 doi

  32. [40]

    Fatahi Bayat , author K

    author F. Fatahi Bayat , author K. Qian , author B. Han , author Y. Sang , author A. Belyy , author S. Khorshidi , author F. Wu , author I. Ilyas , author Y. Li , title FLEEK : Factual error detection and correction with evidence retrieved from external knowledge , in: booktit...

    2023

  33. [41]

    Zhang , author L

    author T. Zhang , author L. Qiu , author Q. Guo , author C. Deng , author Y. Zhang , author Z. Zhang , author C. Zhou , author X. Wang , author L. Fu , title Enhancing uncertainty-based hallucination detection with stronger focus , in: editor H. Bouamor , editor J. Pino , edit...

    2023

  34. [42]

    u ttler , author M. Lewis , author W.-t. Yih , author T. Rockt \

    author P. Lewis , author E. Perez , author A. Piktus , author F. Petroni , author V. Karpukhin , author N. Goyal , author H. K \"u ttler , author M. Lewis , author W.-t. Yih , author T. Rockt \"a schel , et al., title Retrieval-augmented generation for knowledge-intensive nlp ...

    2020

  35. [43]

    Guo , author Y

    author P. Guo , author Y. Hu , author Y. Cao , author Y. Ren , author Y. Li , author H. Huang , title Query in your tongue: Reinforce large language models with retrievers for cross-lingual search generative experience , in: booktitle Proceedings of the ACM Web Conference 2024...

    2024

  36. [45]

    Mou , author Z

    author X. Mou , author Z. Li , author H. Lyu , author J. Luo , author Z. Wei , title Unifying local and global knowledge: Empowering large language models as political experts with knowledge graphs , in: booktitle Proceedings of the ACM Web Conference 2024 , WWW '24, publisher...

    2024

  37. [46]

    Lageweg , author B

    author L. Lageweg , author B. Kruit , title Generative expression constrained knowledge-based decoding for open data , in: booktitle European Semantic Web Conference , organization Springer , year 2024 , pp. pages 307--325

    2024

  38. [47]

    Chataigner , author A

    author C. Chataigner , author A. Ta \" k , author G. Farnadi , title Multilingual hallucination gaps in large language models , journal arXiv preprint arXiv:2410.18270 ( year 2024 )

    2024 arXiv

  39. [48]

    Qi , author R

    author J. Qi , author R. Fern \'a ndez , author A. Bisazza , title Cross-lingual consistency of factual knowledge in multilingual language models , in: editor H. Bouamor , editor J. Pino , editor K. Bali (Eds.), booktitle Proceedings of the 2023 Conference on Empirical Methods...

    2023 doi

  40. [49]

    Kaffee , author R

    author L.-A. Kaffee , author R. Biswas , author C. M. Keet , author E. K. Vakaj , author G. de Melo , title Multilingual Knowledge Graphs and Low-Resource Languages: A Review , journal Transactions on Graph Data and Knowledge volume 1 ( year 2023 ) pages 10:1--10:19 . https://...

    2023 doi

  41. [50]

    Zheng , author W.-L

    author L. Zheng , author W.-L. Chiang , author Y. Sheng , author S. Zhuang , author Z. Wu , author Y. Zhuang , author Z. Lin , author Z. Li , author D. Li , author E. Xing , et al., title Judging llm-as-a-judge with mt-bench and chatbot arena , journal Advances in Neural Infor...

    2023

  42. [51]

    Kang , author T

    author H. Kang , author T. Blevins , author L. Zettlemoyer , title Comparing hallucination detection metrics for multilingual generation , year 2024 . https://arxiv.org/abs/2402.10496. http://arxiv.org/abs/2402.10496 arXiv:2402.10496

    2024 arXiv

  43. [52]

    Min , author K

    author S. Min , author K. Krishna , author X. Lyu , author M. Lewis , author W.-t. Yih , author P. Koh , author M. Iyyer , author L. Zettlemoyer , author H. Hajishirzi , title FA ct S core: Fine-grained atomic evaluation of factual precision in long form text generation , in: ...

    2023

  44. [53]

    2015, , 579, A101

    Aladro, R., Martín, S., Riquelme, D., et al. 2015, , 579, A101

    2015

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.