Pith. sign in

REVIEW 4 major objections 6 minor 27 references

DocMEdit: Towards Document-Level Model Editing

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that document-level model editing—updating a full document with multiple facts—is beyond current model editing methods, which show low accuracy and strong side effects on the new DocMEdit benchmark.

desk verdict A useful new benchmark for document-level model editing, with a plausible central result but soft spots in the extrapolative claim and experimental controls. read the letter →

arxiv 2505.19572 v1 pith:5EZNTVVX submitted 2025-05-26 cs.CL cs.AI

Li Zeng , Zeming Liu , Chong Feng , Heyan Huang , Yuhang Guo This is my paper
classification cs.CLcs.AI
keywords modeleditingdocument-levelbenchmarklargelanguagemodelsknowledgeWikipediaupdatesretrieval-augmentedsideeffects
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

This paper tries to establish that model editing—correcting outdated or false knowledge in large language models—is not yet ready for real-world document revision. Previous editing benchmarks only ask models to output short phrases, so the authors define document-level model editing, where both input and output are full documents, several facts are edited at once, and the updated text cannot be copied straight from the facts. They build DocMEdit, a 37,990-item benchmark drawn from Wikipedia introduction updates, with extracted facts and knowledge-graph triples, and evaluate five editing methods (FT, MEMIT, IKE, SKEME, EREN). The results show all methods have low accuracy on document-level edits while also damaging parts of the document unrelated to the edit. If correct, this means current editing benchmarks overstate how ready models are for realistic knowledge updates.

What carries the argument

The central object is DocMEdit, a dataset of 37,990 document updates built by diffing Wikipedia introduction sections between November 2023 and November 2024. For each update, newly added sentences that mention newly introduced entities are extracted as the facts to be edited, and knowledge-graph triples are extracted and constrained to Wikidata relations. The load-bearing design property is that the target updated document is not directly derivable from the facts alone—the model must combine facts with its existing knowledge—and that multiple facts (2.78 on average) must be integrated into one coherent document. The evaluation machinery is a set of metrics (Document-ROUGE, Document-Entity, Edit-ROUGE, Edit-Entity, and side-effect measures) that separate whether edits landed from whether the untouched parts stayed intact.

What would settle it

Take a random sample of DocMEdit items, compute the ROUGE-L or token-overlap between each target updated sentence and its supporting fact; if the overlap is consistently high (e.g., above 80%), the extrapolative property is falsified. Alternatively, run a baseline that retrieves the correct fact and inserts its verbatim sentence into the document at the right position; if this baseline achieves near-perfect Document-Entity scores, the paper's conclusion that existing methods cannot handle document-level editing would need to be reinterpreted as a retrieval problem.

Watch

Extended reading notes

Core claim

The paper's central claim is that document-level model editing is a distinct, harder regime than the short-output tasks used in prior editing benchmarks, and that existing methods fail in this regime. On DocMEdit, all evaluated methods—parameter-modification approaches (FT, MEMIT) and retrieval-augmented approaches (IKE, SKEME, EREN)—achieved low document- and edit-level accuracy while exhibiting strong side effects: they altered or lost more than 40% of entities in parts of the document unrelated to the edit. The paper further claims that performance declines with longer contexts, longer facts, a larger number of facts per edit, and with successive fact updates, indicating that the difficulty comes from scaling editing to realistic document-level settings.

Load-bearing premise

The benchmark's novelty rests on the claim that the target updated document cannot be derived by simply copying or stitching the provided facts; if that claim fails, the task may be much easier than the paper assumes, and the measured failures would reflect poor retrieval or instruction following rather than a hard extrapolation problem.

Editorial extensions

If this is right

  • If the central claim is right, today's editing benchmarks give an inflated picture: short-phrase accuracy does not transfer to full-document revision.
  • Parameter-modification methods (FT, MEMIT) can degrade a model's generation quality and fail more severely on document-level edits than RAG-based methods.
  • The analysis identifies the concrete scaling axes—context length, fact length, number of facts, and successive updates—that any practical editing method must handle.
  • The finding that all methods suffer strong side effects on unchanged portions implies that locality, not just edit success, should be the primary target for document-level editing.
  • The dataset plus metrics give a shared testbed for future methods aimed at real-world knowledge updates.

Reading between the lines

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

  • The 'extrapolative' property is asserted rather than demonstrated; if the updated sentences turn out to be near-verbatim stitches of the provided facts, the task collapses to fact-copying and the failure of existing methods would be less about extrapolation and more about retrieval or instruction following.
  • A useful extension would be to test a strong retrieval-plus-copy baseline: retrieve the relevant fact and paste its sentence into the document at the right position; if this baseline beats FT and MEMIT, it confirms that the bottleneck is fact selection, not knowledge update.
  • The paper's claim that longer facts hurt IKE because vector retrieval degrades suggests a testable prediction: changing the retriever to entity-based retrieval (like SKEME) should reduce the length penalty.
  • The side-effect metrics rely on entity overlap; an extension could measure semantic faithfulness of preserved sentences to ensure that 'unchanged' entities are not embedded in rewritten, meaning-shifted sentences.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper introduces document-level model editing as a new task and presents DocMEdit, a large-scale benchmark constructed from Wikipedia page-diff data (37,990 items, 105,652 facts). The benchmark is designed to require document-level inputs and outputs, multiple edit facts per item, and an 'extrapolative' property in which the updated document cannot be derived from the facts alone. The authors propose four accuracy metrics (DR, DE, ER, EE), two side-effect metrics (RSE, ESE), and a semantic-coherence scale, then evaluate several existing editing methods (FT, MEMIT, IKE, SKEME, EREN) on GPT-2 XL, GPT-J, Llama-2, Mistral, and DeepSeek. The main empirical claim is that existing methods have low accuracy and strong side effects on this benchmark. Additional analyses study the influence of context length, fact length, number of facts, and fact updates on editing performance.

Significance. If the claims are supported, DocMEdit is a valuable new resource: it is one of the first benchmarks to evaluate model editing at the document level, it is large and publicly released, and it raises a realistic problem for practitioners. The paper also contributes a systematic comparison of parameter-modification and retrieval-augmented editing methods under longer contexts and multi-fact edits. The benchmark's novelty hinges on the 'extrapolative' property, which is currently asserted but not verified; the proposed metrics are also not defined with enough precision to guarantee reproducibility. Because these are load-bearing for the central conclusion, the paper requires revision rather than acceptance in its current form.

major comments (4)
  1. [§4.3.1, Table 1, Table 6, Table 11] The 'extrapolative' property is claimed as a distinguishing feature of DocMEdit, but the paper neither defines it operationally nor provides any empirical evidence for it. The examples in Table 6 and Table 11 show target additions that are almost verbatim copies or simple stitchings of the provided facts (e.g., the fact in Table 11 directly gives the target sentence with only light rephrasing). If the task reduces to fact-copying or context insertion, then the low scores of editing methods may reflect their difficulty with long-form insertion from retrieved context rather than a document-level editing deficiency. Please report quantitative measures of how much of the target's new content is lexically or semantically recoverable from the facts alone (e.g., ROUGE-L/entity overlap between the added sentences in y' and the concatenated facts F), and, if needed, revise the extrapolation claim to match the measured degree.
  2. [Appendix C.2 and §6.3 (RQ2a)] The RAG baselines (IKE, SKEME, EREN) are restricted to at most the top-5 retrieved facts, whereas FT and MEMIT receive all editable facts. Because DocMEdit contains 7,226 items with five or more facts (Table 7), the comparison is not controlled for the number of provided facts. This directly confounds the RQ2a analysis in Figure 5: the claim that RAG methods rapidly deteriorate when the number of facts reaches five or more is expected from the top-5 truncation alone. Please either remove the top-k limit for RAG methods where the context window permits, restrict all methods to the top-5 facts for those items, or explicitly control for retrieval coverage in the analysis.
  3. [Appendix C.4] The metric definitions are mathematically ill-specified and not reproducible as written. For example, EE is stated as 'EE = A ∩ Bi / len(Bi)' without defining A and Bi as sets, without absolute-value notation for cardinality, and with no statement of the expected range (e.g., 0-1 or 0-100). Similarly, 'Update(a,b)' and 'Res(a,b)' are used inconsistently between text-based and entity-based metrics: for DR, ROUGE requires 'Update' to return text, while for DE/EE it returns sets of extracted triples. Please provide rigorous set-theoretic definitions for each metric, specify whether precision and recall are combined or reported separately, and give concrete examples of how a single target sentence and a candidate output are scored.
  4. [§4.2] The quality-control section reports 'a DR of 81.17 and a DE of 89.71' without describing the comparison protocol. It is unclear what 'annotated data' and 'automatically collected text' refer to, whether the metrics are computed between the automatically extracted target and a human-annotated gold update, and how many items were annotated. Please clarify the procedure, report the number of human-annotated samples, and state the confidence intervals or inter-annotator agreement if available.
minor comments (6)
  1. [Abstract] The sentence beginning 'However, most existing datasets...' contains a subject-verb agreement error: 'overlooks' should be 'overlook'.
  2. [Table 1] The table uses '%' and '"' markers that are not rendered correctly; the legend should use unambiguous symbols for 'yes' and 'no', and the row for WIKIBIO needs a clear indication of which properties it satisfies.
  3. [§3] The notation y = Pn i=1 si is broken in the rendered text; please define y as the concatenation of sentences s_i and provide a formal definition of the edit operation.
  4. [§5.3] There are typos: 'thest metrics' should be 'these metrics' in two places.
  5. [Table 11] The target sentence 'It is locally overlain by of the Andersons Cove Formation' contains a grammatical error ('by of'); this example is used to illustrate the extrapolative property, so it should be clean.
  6. [§1] The claim 'we are the first to propose the document-level task for model editing' should be softened or supported with a more thorough comparison to Wu et al. (2024) and other long-form editing work, since novelty claims are hard to establish absolutely.

Circularity Check

1 steps flagged · score 3.0 of 10

DocMEdit's empirical benchmark results are measured, not fitted, but the advertised 'extrapolative' property is contradicted by the fact-extraction construction, making the novelty framing partly circular.

  1. self definitional [Section 4.1.2 (Facts Collection), Section 4.3.1 (Benchmark Analysis), Table 1 caption]
    "for each sentence, if an entity mentioned in the sentence was newly introduced in the document update, the sentence was considered to support the document update fi of the corresponding entity. ... 'Extrapolative' refers to whether the answer to each question requires inference based on the existing knowledge within the LLM, or if it can be directly derived from the given facts alone. ... Additionally, it is uniquely characterized by the feature that the expected output cannot be directly derived from the facts to be edited."

    By construction (Section 4.1.2), each fact f_i is a sentence newly added in the target document y' between two Wikipedia snapshots. The target's new content is therefore just the fact set F (up to light paraphrase or subject adaptation), so the expected output can be derived from F by insertion. Yet Table 1 defines 'extrapolative' as the answer not being directly derivable from the given facts, and Section 4.3.1 claims DocMEdit is 'uniquely characterized' by exactly that property.

full rationale

The core benchmark construction and evaluation are externally grounded: DocMEdit is mined from Wikipedia diffs between two dump dates (Section 4.1.1), facts are extracted from the updated text, and the main accuracies in Table 3 are measured rather than fitted. No parameter is fitted to the evaluation data, and the claim that 'existing methods have low accuracy while exhibiting strong side effects' is a direct empirical observation, not the output of an equation that encodes the same observation. The paper's self-citations (FAME, SKEME, and related prior work) are frequent but not load-bearing for the central result: SKEME is one of several baselines whose outputs are measured, and the conclusion that current editing methods struggle on document-level edits does not depend on accepting any particular claim from those earlier papers. The one definitional concern is the advertised 'extrapolative' property. Section 4.1.2 defines each supporting fact as a sentence newly added in the target document, so the target's new content is essentially the fact set itself; Section 4.3.1 and Table 1 then define DocMEdit's uniqueness as the expected output not being directly derivable from the facts. The running example in Table 11 confirms near-verbatim conversion. This makes the extrapolative characterization a re-description of the construction rather than an independently verified property, and it weakens the interpretation that existing methods fail specifically because of extrapolation. It does not, however, make the accuracy measurements themselves circular, so the overall circularity burden is low. The limitation statement about long inputs and computational cost is a practical limitation, not a circular step.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The benchmark rests on several domain assumptions about Wikipedia diffs, the mapping from new entities to facts, and the reliability of triple extraction for evaluation. The 'extrapolative' property is an ad hoc assumption that is not verified. No free parameters are fitted to data; the listed parameters are hand-chosen design thresholds that affect results.

free parameters (2)
  • top_k for RAG retrieval = 5
    Chosen to keep prompts within context limits; affects RAG method results and the comparison with FT/MEMIT.
  • entity-addition filter threshold = >=1 newly added entity
    Hand-chosen filter for retaining Wikipedia diffs; determines which updates enter the benchmark and may bias task difficulty.
assumptions (4)
  • domain assumption Wikipedia Introduction-section diffs between 2023-11-01 and 2024-11-01, filtered to entity additions, represent a realistic sample of document-level knowledge edits
    Used in Section 4.1.1; if unrepresentative, the benchmark's external validity fails.
  • domain assumption Sentences mentioning newly added entities in the updated document are valid supporting facts for the target update
    Used in Section 4.1.2, following Iv et al. (2022); errors in this mapping propagate into the gold outputs and evaluation.
  • ad hoc to paper The expected output cannot be inferred from the given facts (extrapolative property)
    Asserted in Sections 1 and 4.3.1, but not formally demonstrated; the running example (Table 6) appears to contradict it.
  • domain assumption Open Information Extraction constrained by Wikidata relations accurately captures semantic updates for entity metrics
    Used in Section 4.1.3 and metric definitions in Appendix C.4; brittle extraction can make DE/EE reflect extractor behavior rather than editing success.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DocMEdit: Towards Document-Level Model Editing." pith.science (2026). https://pith.science/paper/5EZNTVVX

@misc{pith2026250519572,
  author       = {Pith},
  title        = {Pith review of: DocMEdit: Towards Document-Level Model Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5EZNTVVX}},
  note         = {Machine review of arXiv:2505.19572}
}
read the original abstract

Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost. Prior research has proposed a variety of datasets to assess the effectiveness of these model editing methods. However, most existing datasets only require models to output short phrases or sentences, overlooks the widespread existence of document-level tasks in the real world, raising doubts about their practical usability. Aimed at addressing this limitation and promoting the application of model editing in real-world scenarios, we propose the task of document-level model editing. To tackle such challenges and enhance model capabilities in practical settings, we introduce \benchmarkname, a dataset focused on document-level model editing, characterized by document-level inputs and outputs, extrapolative, and multiple facts within a single edit. We propose a series of evaluation metrics and experiments. The results show that the difficulties in document-level model editing pose challenges for existing model editing methods.

Figures

Figures reproduced from arXiv: 2505.19572 by the authors.

Figure 1
Figure 1. An example of DocMEdit. The input and output of DocMEdit are both document-level contents. Model editing should inject multiple facts to be edited into the model, enabling the edited model to output the updated document. legal advice, highlighting the importance of meth￾ods to correct errors in LLMs. To address this issue without expensive retraining, model editing tech￾niques have been proposed (Mitchell et al., 20… view at source ↗
Figure 2
Figure 2. The construction process of DocMEdit. In [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. result of RQ1a. The x-axis represents the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: result of RQ1b. The x-axis represents the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: result of RQ2a. The x-axis represents the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: gives an example from DocMEdit, where "Input" and "Target" correspond to the document before and after the update, respectively. "Facts" represents the facts to be edited, "Title" denotes the title of the document, which is also used as the subject during the update pr…
Figure 7
Figure 7. Figure 7: The distribution of fact length [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Prompt for w/o Edit, FT and MEMIT. "Few-shot input" and "few-shot output" refer to the input and output [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Prompt for IKE and SKEME. "Few-shot input" refers to the input of a few-shot samples. "Few-shot [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Prompt for EREN. System instructions were added to the figure [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

27 extracted references · 19 canonical work pages

  1. [4]

    arXiv preprint arXiv:2406.12331

    Re- trieval meets reasoning: Dynamic in-context edit- ing for long-text understanding. arXiv preprint arXiv:2406.12331. Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Gold- berg

    arXiv

  2. [6]

    In Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 5484–5495, Online and Punta Cana, Dominican Republic

    Transformer feed-forward layers are key- value memories. In Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 5484–5495, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen- Hua Ling, Kai-Wei Chang, and Nanyun Peng

    work page 2021

  3. [7]

    In Pro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 16801– 16819, Miami, Florida, USA

    Model editing harms general abilities of large lan- guage models: Regularization to the rescue. In Pro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 16801– 16819, Miami, Florida, USA. Association for Com- putational Linguistics. Akshat Gupta, Sidharth Baskaran, and Gopala Anu- manchipalli

    work page 2024

  4. [8]

    In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 21738–21744, Miami, Florida, USA

    Rebuilding ROME : Resolving model collapse during sequential model editing. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 21738–21744, Miami, Florida, USA. Association for Computational Linguistics. Robert Iv, Alexandre Passos, Sameer Singh, and Ming- Wei Chang

    work page 2024

  5. [9]

    FRUIT: Faithfully reflecting up- dated information in text. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3670–3686, Seattle, United States. Association for Computational Lin- guistics. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men- sch, Chris ...

    work page 2022

  6. [11]

    In Proceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 9004–9017, Singapore

    SelfCheckGPT: Zero-resource black-box hallucina- tion detection for generative large language models. In Proceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 9004–9017, Singapore. Association for Computa- tional Linguistics. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov

    work page 2023

  7. [12]

    Preprint, arXiv:2404.05520

    The fact selection problem in llm- based program repair. Preprint, arXiv:2404.05520. Zafaryab Rasool, Stefanus Kurniawan, Sherwin Balugo, Scott Barnett, Rajesh Vasa, Courtney Chesser, Ben- jamin M Hampstead, Sylvie Belleville, Kon Mouza- kis, and Alex Bahar-Fuchs

    arXiv

  8. [13]

    In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural lan- guage learning, pages 523–534

    Open language learning for in- formation extraction. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural lan- guage learning, pages 523–534. Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, and Artem Babenko

    work page 2012

Show all 27 references
  1. [15]

    https://github.com/kingoflolz/mesh-transformer-jax

    GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax. Ke Wang, Jiahui Zhu, Minjie Ren, Zeming Liu, Shi- wei Li, Zongye Zhang, Chenkai Zhang, Xiaoyu Wu, Qiqi Zhan, Qingjie Liu, et al. 2024a. A survey on data synthesis...

    arXiv

  2. [16]

    In Proceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 16646–16661, Singapore

    Document-level machine translation with large lan- guage models. In Proceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 16646–16661, Singapore. Association for Computational Linguistics. Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun...

    2023

  3. [19]

    In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 15118–15133

    Akew: Assessing knowledge editing in the wild. In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 15118–15133. Jiakuan Xie, Pengfei Cao, Yuheng Chen, Yubo Chen, Kang Liu, and Jun Zhao

    2024

  4. [20]

    In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10222–10240, Singapore

    Editing large language models: Prob- lems, methods, and opportunities. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10222–10240, Singapore. Association for Computational Linguis- tics. Li Zeng, Yingyu Shan, Zeming Liu, Jiashu...

    2023

  5. [21]

    In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 15992–16011, Miami, Florida, USA

    FAME: Towards factual multi- task model editing. In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 15992–16011, Miami, Florida, USA. Association for Computational Linguistics. Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, S...

    2024 arXiv

  6. [22]

    arXiv preprint arXiv:2410.15999

    Steer- ing knowledge selection behaviours in llms via sae- based representation engineering. arXiv preprint arXiv:2410.15999. Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang

    arXiv

  7. [23]

    Association for Computa- tional Linguistics

    Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 4862–4876, Singapore. Association for Computa- tional Linguistics. Hanwen Zheng, Sijia Wang, and Lifu Huang

    2023

  8. [24]

    Input" and

    MQuAKE: Assessing knowledge editing in language models via multi-hop questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Lan- guage Processing, pages 15686–15702, Singapore. Association for Computational Linguistics. A Terminology Explanation In th...

    2023

  9. [25]

    Facts: [’Yuxari Quscular INTRODUCTION It was part of Shusha District with Malibeyli and Ashaghy Gushchular villages till 5 December 2023.’, ’Malibeyli IN- TRODUCTION It was part of Shusha District with Asagi Quscular and Yuxari Quscular villages till 5 December 2023.’] Inputs_...

    2022

  10. [26]

    Few-shot input

    setup and use topk = 5to ensure a fair comparison. Table 10 shows the results of data retrieval for several RAG methods. It can be observed that IKE and EREN, which retrieve data via vec- tor databases, have poorer retrieval results, while SKEME, which retrieves facts through ...

    1987

  11. [42]

    data" refers to the input data, and

    and near Gogama, Ontario (Lorna, 1979; Ridgway, 1985). FT The Belle Bay Formation is a metamorphosed formation cropping out in New- foundland, Canada. It is a member of the North America-wide group of rock formations known as the Taconian Orogeny. The Belle Bay Formation is ov...

    1979

  12. [2012]

    In Proceedings of COLING 2012, pages 711–726, Mumbai, India

    A corpus-based study of edit categories in featured and non-featured Wikipedia articles. In Proceedings of COLING 2012, pages 711–726, Mumbai, India. The COLING 2012 Organizing Committee. Nicola De Cao, Wilker Aziz, and Ivan Titov

    2012

  13. [2019]

    ArXiv, abs/1908.09203

    Release strategies and the social impacts of language models. ArXiv, abs/1908.09203. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Jouli...

    1908 arXiv

  14. [2020]

    In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online

    Transform- ers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. Xiaobao Wu, Liangming Pan, William Ya...

    2020

  15. [2021]

    In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing , pages 6491– 6506, Online and Punta Cana, Dominican Republic

    Edit- ing factual knowledge in language models. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing , pages 6491– 6506, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Weizhi Fei, Xueyan Niu, Guoqi...

    2021

  16. [2022]

    In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Process- ing, pages 30–45, Abu Dhabi, United Arab Emirates

    Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Process- ing, pages 30–45, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics...

    2022

  17. [2023]

    Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer

    Mistral 7b. Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017a. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 333–342, Vancouver, Canada. Association f...

    2017 arXiv

  18. [2024]

    In Proceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 14157–14172, Torino, Italia

    Robust and scalable model edit- ing for large language models. In Proceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 14157–14172, Torino, Italia. ELRA and ICCL. Johannes Daxenberg...

    2024

  19. [2025]

    arXiv preprint arXiv:2503.08963

    Gradient-guided attention map editing: Towards efficient contextual hallucina- tion mitigation. arXiv preprint arXiv:2503.08963. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, ...

    arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.