Pith. sign in

REVIEW 4 major objections 5 minor 53 references

SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read The paper claims that SKETCH, a hybrid retriever combining semantic text chunks and a knowledge graph, outperforms baseline retrieval methods on four question-answering datasets.

desk verdict SKETCH's own tables refute its 'consistently outperforms' claim, and the evaluation protocol is circular — the paper fails on its own evidence. read the letter →

arxiv 2412.15443 v1 pith:HQ3JQVCJ submitted 2024-12-19 cs.CL

Aakash Mahalingam , Vinesh Kumar Gande , Aman Chadha , Vinija Jain , Divya Chaudhary This is my paper · ORCID
classification cs.CL
keywords Retrieval-AugmentedGenerationknowledgegraphssemanticchunkinghybridretrievallong-documentcomprehensionmulti-hopreasoningquestionansweringevaluation
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

SKETCH is a retrieval method for question-answering systems that fuses two retrieval channels: semantically coherent text chunks embedded as vectors, and a knowledge graph of entities and relationships built from the same corpus. The paper's claim is that this hybrid retriever captures both the local meaning of passages and the long-range structure connecting them, so it can answer questions whose evidence is scattered across long documents. If the claim is right, retrieval-augmented generation systems would retrieve more relevant context, ground their answers in richer structure, and score higher on answer relevancy and context precision. The authors report consistent gains over naive retrieval, semantic-only retrieval, knowledge-graph-only retrieval, and a hierarchical baseline across four datasets.

What carries the argument

The mechanism is a dual-channel retriever. One channel splits documents into semantically coherent chunks, further splits them into fixed-size overlapping segments, embeds them in a vector store, and retrieves by similarity to the query. The other channel constructs a knowledge graph from the same documents by extracting entities and relationships, then identifies query entities by named entity recognition and follows graph edges to gather related nodes. The retrieved contexts from both channels are merged into a single prompt for the generator, and passages that appear in both structured and unstructured results are treated as confirmation signals that boost their relevance.

What would settle it

Run the same five retrieval methods on a larger, independently constructed question set with human-verified ground truth, and score answers with a judge different from the models used to build the system. If the hybrid method no longer shows consistent gains in answer relevancy and context precision, the paper's headline claim fails. A quick local check is to expand the nine-question Italian Cuisine test set and see whether the 0.94 and 0.99 scores remain stable.

Watch

Extended reading notes

Core claim

The central finding is that merging structured and unstructured retrieval outperforms any single channel. Concretely, on the QuALITY, QASPER, NarrativeQA, and a small Italian Cuisine corpus, the hybrid method improves answer relevancy and context precision over all baselines in most comparisons, while keeping faithfulness competitive. On the Italian Cuisine test set the hybrid method reaches an answer relevancy of 0.94 and a context precision of 0.99, the highest scores reported in the paper.

Load-bearing premise

The entire comparison depends on the assumption that the automated judge and the small, self-created test set used to measure retrieval quality are accurate and unbiased; if the judge is noisy or the test set unrepresentative, the reported improvements could be an artifact of the evaluation rather than a real retrieval gain.

Editorial extensions

If this is right

  • Long-document question answering should become more reliable, because the knowledge graph can bridge evidence that sits in distant chunks.
  • Multi-hop questions, which require chaining facts across paragraphs, are the natural target: graph traversal supplies the missing links.
  • The hybrid design could be applied to any corpus where entities and relations can be extracted, including scientific literature, legal documents, and enterprise knowledge bases.
  • The reported gains come with a cost: constructing and querying a knowledge graph adds overhead compared with naive chunk retrieval.

Reading between the lines

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

  • The Italian Cuisine comparison rests on only nine self-generated questions, so the headline scores of 0.94 and 0.99 should be read as an existence proof rather than a stable estimate; a larger test set could narrow or erase the gap.
  • Because the same language-model family is used for entity extraction, graph construction, and judging, some of the reported advantage may reflect the judge rewarding the system's own phrasing rather than independent retrieval quality.
  • The method's value likely scales with document length and question complexity; on short, single-paragraph queries the extra graph channel may add noise without benefit.
  • A direct comparison on an independently constructed multi-hop benchmark with human-verified ground truth would clarify whether the claimed gains generalize beyond the four chosen datasets.
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 / 5 minor

Summary. The paper introduces SKETCH, a retrieval-augmented generation method that combines semantic text chunking with a knowledge graph, and evaluates it on four datasets (Italian Cuisine, QuALITY, QASPER, NarrativeQA) using RAGAS metrics with GPT-3.5-turbo as the judge. The central claim, stated in the Abstract and Section 5, is that SKETCH 'consistently outperforms' baseline approaches on answer_relevancy, faithfulness, context_precision, and context_recall. The paper reports tables of RAGAS metrics and F1 scores for Naive RAG, RAPTOR, Semantic-only, KG-only, and SKETCH, and discusses strengths in answer relevancy and context precision while acknowledging some weaknesses in faithfulness and context recall.

Significance. If the central claim were correct, SKETCH would be a useful contribution to hybrid retrieval for long-document and multi-hop question answering, where combining structured and unstructured evidence is an active research direction. The paper's transparency in reporting per-dataset tables is a strength, because those tables make the results checkable. However, the tables directly contradict the 'consistently outperforms' headline, and the evaluation protocol has serious threats to validity: a nine-question self-generated test set scored with the same framework used to generate it, an LLM judge known to be prompt-sensitive, and no error bars or multiple runs. The paper also contains a clear factual misreport of a baseline result in the NarrativeQA section. Because the reported evidence fails to support the central claim, the contribution as presented is not established.

major comments (4)
  1. [Abstract and Tables 1–4] The Abstract's claim that SKETCH 'consistently outperforms baseline approaches on key RAGAS metrics' is contradicted by the paper's own tables. Table 1 shows SKETCH below Naive RAG on faithfulness (0.87 vs. 1.00) and context_recall (0.72 vs. 0.88), and below Semantic-only on F1 (0.83 vs. 0.87). Table 2 shows SKETCH below Naive RAG on faithfulness (0.69 vs. 0.83). Table 3 shows SKETCH below KG-only on context_precision (0.67 vs. 0.71), context_recall (0.49 vs. 0.60), and F1 (0.57 vs. 0.65). Table 4 shows SKETCH below KG-only on context_precision (0.51 vs. 0.58), context_recall (0.46 vs. 0.47), and F1 (0.48 vs. 0.52). Thus the central assertion fails even under the paper's own selected metrics and selected baselines, independent of any debate about LLM-based evaluation.
  2. [Section 4.4 (NarrativeQA)] The prose misreports the RAPTOR baseline. It states that SKETCH's answer_relevancy of 0.50 'slightly outperformed RAPTOR and KG-only, both at 0.47,' but Table 4 lists RAPTOR's answer_relevancy as 0.10, not 0.47; only KG-only is 0.47. This is a factual error in the reporting of a baseline result and suggests that the narrative discussion was not checked against the accompanying table. It undermines confidence in the other qualitative comparisons in the results sections.
  3. [Sections 3.3 and 4.1 (Italian Cuisine dataset)] The Italian Cuisine evaluation is circular in a load-bearing way: the nine-question test set and ground truth were generated using the RAGAS framework, and the same RAGAS metrics, judged by GPT-3.5-turbo, are then used to score all methods on that test set. The paper provides no details of the question-generation prompt, no human validation of the generated questions or ground truth, and no release of the test set. Since the headline results (answer_relevancy 0.94 and context_precision 0.99 on Italian Cuisine) come from this dataset, the evidence for the central claim is not independently checkable. A concrete fix would be to release the test set with human-verified answers or to replace this dataset with an established benchmark.
  4. [Sections 3.2.2.3 and 5 (Hybrid retrieval and evaluation)] The hybrid retrieval mechanism is specified only at a high level: Section 3.2.2.3 says the structured and unstructured results are 'combined' and that semantic alignment is prioritized, but it does not state the merge rule, the number of retrieved chunks or graph nodes, the graph traversal depth, or the prompt template used for generation. Additionally, all reported numbers appear to be single-run point estimates, and Section 5 itself acknowledges variance from GPT sampling and prompt sensitivity. Without multiple runs, confidence intervals, or a detailed protocol, the claim of 'consistent' outperformance cannot be statistically supported even in the cases where the point estimates favor SKETCH.
minor comments (5)
  1. [Section 1] The Introduction refers to the dataset as 'QAER [2; 8]' instead of 'QASPER'; the typo should be fixed.
  2. [Sections 3.2.2.1, 4.1, and 5] There are several typographical errors: 'plausibe' should be 'plausible' (§3.2.2.1), 'parargraphs' should be 'paragraphs' (§4.1), and 'xapplicability' should be 'applicability' (§5).
  3. [References] The reference list contains questionable entries: reference [15] is titled 'Levels of text splitting' but is cited for knowledge graphs, and reference [3] begins with 'EMozilla authored'; several reference entries also lack authors, venues, or complete publication data.
  4. [Figures] The figure cross-referencing is inconsistent: Section 3.2 refers to Figure 2 for the architecture, Section 4 says results are 'visualized in Figure 2,' but Figure 1 in the appendix appears to contain the RAGAS metrics plot; the figure numbers and captions should be reconciled.
  5. [Table 1] The caption of Table 1 lists the approaches as 'Naive RAG, RAPTOR, SKETCH, Semantic-only, and KG-only' while the table rows are ordered Naive RAG, Semantic, KG, RAPTOR, SKETCH; the ordering should be aligned and consistent across tables.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the concerns raised are evaluative validity and internal consistency, not circular derivation.

full rationale

I found no load-bearing circularity in SKETCH. The method is an architecture with no fitted parameters; it is evaluated on three external benchmarks (QuALITY, QASPER, NarrativeQA) and one self-generated Italian Cuisine set. The Italian Cuisine set is generated with the RAGAS framework and then scored with RAGAS metrics using GPT-3.5-turbo as judge (Sections 3.3, 4, 4.1), which is self-referential for that dataset and a real reproducibility/validity risk—explicitly acknowledged in Section 5: 'SKETCH’s dependence on GPT models for query parsing and RAGAS evaluation can introduce errors and variance due to sampling randomness, prompt sensitivity, and occasional hallucinations.' However, this does not make the reported outputs equivalent to the inputs by construction, and no fitted parameter is renamed as a prediction. The abstract's claim of consistent outperformance is contradicted by the paper's own Tables 1-4 (e.g., SKETCH's context_recall trails Naive RAG on Italian Cuisine and trails KG-only on QASPER and NarrativeQA), but that is an internal-consistency/correctness problem, not circular reasoning. No self-citation chain, imported uniqueness theorem, or ansatz-smuggling citation is load-bearing. Accordingly, the circularity score is 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The reported results rest on hand-chosen chunking parameters, default semantic-splitter thresholds from an external blog post, and unvalidated assumptions about GPT-4 NER accuracy and GPT-3.5 judge reliability. The self-generated Italian Cuisine test set adds an ad hoc benchmark into the loop.

free parameters (3)
  • chunk_size = 100 tokens
    Recursive text splitter chunk size is hand-chosen; no ablation or sensitivity analysis is reported (Section 3.2.1.3).
  • overlap_window = 16 tokens
    Overlap between recursive chunks is hand-chosen; no analysis of its effect on retrieval is given (Section 3.2.1.3).
  • semantic_splitter_threshold = 95th percentile of distance differences from reference [22]
    The semantic splitter threshold T is inherited from Kamradt's blog post, not derived or tuned for these datasets (Section 2.2.1).
assumptions (3)
  • domain assumption GPT-4 extracts accurate named entities from queries and text for KG construction
    The structured retriever depends on NER quality; no manual verification or error analysis is reported (Section 3.2.2.1).
  • domain assumption RAGAS metrics scored by GPT-3.5-turbo faithfully measure retrieval and generation quality
    All reported numbers come from an LLM judge; the authors acknowledge variance and prompt sensitivity in the Limitations section.
  • ad hoc to paper The 9-question Italian Cuisine test set generated with RAGAS is a valid benchmark
    The test set is constructed by the authors using the same framework that computes the metrics, creating a closed loop (Section 4.1).

how reviews work

0 comments
Cite this review

Pith. "Pith review of SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval." pith.science (2026). https://pith.science/paper/HQ3JQVCJ

@misc{pith2026241215443,
  author       = {Pith},
  title        = {Pith review of: SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HQ3JQVCJ}},
  note         = {Machine review of arXiv:2412.15443}
}
read the original abstract

Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a comprehensive understanding of the context. This paper introduces SKETCH, a novel methodology that enhances the RAG retrieval process by integrating semantic text retrieval with knowledge graphs, thereby merging structured and unstructured data for a more holistic comprehension. SKETCH, demonstrates substantial improvements in retrieval performance and maintains superior context integrity compared to traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER, NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline approaches on key RAGAS metrics such as answer_relevancy, faithfulness, context_precision and context_recall. Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99, representing the highest performance across all evaluated metrics. These results highlight SKETCH's capability in delivering more accurate and contextually relevant responses, setting new benchmarks for future retrieval systems.

Figures

Figures reproduced from arXiv: 2412.15443 by the authors.

Figure 1
Figure 1. RAGAS metrics against all datasets and approaches retrieval results, surpassing the limitations seen in isolated semantic or structured retrieval methods. 4.2 QuALITY Dataset The QuALITY validation dataset, designed for long-form document comprehension, provided a challenging evaluation ground for retrieval￾augmented systems. Despite the complexity of extended passages, SKETCH outperformed all baseline methods, demo… view at source ↗
Figure 2
Figure 2. Architecture of SKETCH with a Hybrid Retriever combining structured and unstructured retrievers [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Italian Cuisine KG Representation [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Naive RAG Italian Cuisine Performance Heatmap [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Semantic only Italian Cuisine Performance Heatmap [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: KG only Italian Cuisine Performance Heatmap [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: RAPTOR Italian Cuisine Performance Heatmap [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: SKETCH Italian Cuisine Performance Heatmap [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

53 extracted references · 30 canonical work pages

  1. [1]

    Narrativeqa

    Deepmind authored Hugging Face Narra- tiveQA dataset. Narrativeqa

    work page

  2. [2]

    AllenAI authored Hugging Face QASPER dataset. Qasper

    work page

  3. [3]

    EMozilla authored Hugging Face QuALITY dataset. Quality

    work page

  4. [4]

    Jinheon Baek, Alham Fikri Aji, Jens Lehmann, and Sung Ju Hwang. 2023. Direct fact retrieval from knowledge graphs without entity linking. Preprint, arXiv:2305.12416

    work page Pith review arXiv 2023

  5. [5]

    Rae, Erich Elsen, and Laurent Sifre

    Sebastian Borgeaud, Arthur Mensch, Jordan Hoff- mann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hen- nigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geof- frey Irvin...

    work page

  6. [6]

    Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, and Jie Fu. 2024. Rq-rag: Learning to refine queries for retrieval augmented generation. Preprint, arXiv:2404.00610

    arXiv 2024

  7. [7]

    Arman Cohan and Nazli Goharian. 2017. Contextu- alizing citations for scientific summarization using word embeddings and domain knowledge. In Pro- ceedings of the 40th International ACM SIGIR Con- ference on Research and Development in Information Retrieval, SIGIR ’17, page 1133–1136, New York, NY , USA. Association for Computing Machinery

    work page 2017

  8. [8]

    Smith, and Matt Gardner

    Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, and Matt Gardner. 2021. A dataset of information-seeking questions and answers anchored in research papers. Preprint, arXiv:2105.03011

    arXiv 2021

Show all 53 references
  1. [10]

    Questions are all you need to train a dense passage retriever

    Dani Yogatama Luke Zettlemoyer Joelle Pineau Manzil Zaheer Devendra Singh Sachan, Mike Lewis. Questions are all you need to train a dense passage retriever

  2. [11]

    Raftery A. E. Fraley, C. Model-based clustering, discriminant analysis, and density estimation

  3. [12]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024. Retrieval-augmented gener- ation for large language models: A survey. Preprint, arXiv:2312.10997

    2024 arXiv

  4. [14]

    Finite mixture models

    David Peel Geoffrey McLachlan. Finite mixture models

  5. [15]

    Levels of text splitting

    Aidan Hogan. Levels of text splitting

  6. [16]

    Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Se- queda, Steffen Staab, and Antoine Zimmermann

    Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel- Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Se- q...

  7. [17]

    Yizheng Huang and Jimmay Huang. 2024. A sur- vey on retrieval-augmented text generation for large language models. Preprint, arXiv:2404.10981

    2024 arXiv

  8. [18]

    Ali Hur, Naeem Janjua, and Mohiuddin Ahmed

  9. [19]

    Preprint, arXiv:2110.08012

    A survey on state-of-the-art techniques for knowledge graphs construction and challenges ahead. Preprint, arXiv:2110.08012

    arXiv

  10. [20]

    What is a knowledge graph??

    IBM. What is a knowledge graph??

  11. [21]

    Gautier Izacard and Edouard Grave. 2022. Distill- ing knowledge from reader to retriever for question answering. Preprint, arXiv:2012.04584

    2022 arXiv

  12. [22]

    A statistical interpretation of term specificity and its application in retrieval

    Sparck Jones. A statistical interpretation of term specificity and its application in retrieval

  13. [23]

    Levels of text splitting

    Greg Kamradt. Levels of text splitting

  14. [24]

    Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020. Gener- alization through memorization: Nearest neighbor language models. Preprint, arXiv:1911.00172

    2020 arXiv

  15. [25]

    Tomáš Ko ˇciský, Jonathan Schwarz, Phil Blun- som, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2017. The narra- tiveqa reading comprehension challenge. Preprint, arXiv:1712.07040

    2017 arXiv

  16. [26]

    Patrick Lewis, Ethan Perez, Aleksandra Pik- tus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. Retrieval-augmented genera- tion for knowledge-intensive nlp tasks. Preprint, a...

    2021 arXiv

  17. [27]

    Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2022. Large language mod- els with controllable working memory. Preprint, arXiv:2211.05110

    2022 arXiv

  18. [28]

    Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowd- hury, Yun Li, Hejie Cui, Xuchao Zhang, Tian- jiao Zhao, Amit Panalkar, Dhagash Mehta, Ste- fano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White,...

  19. [29]

    Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, and Kyle Lo. 2023. A question answering framework for decontextualizing user- facing snippets from scientific documents. Preprint, arXiv:2305.14772

    2023 arXiv

  20. [30]

    Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikr- ishna Karanam, and Sumit Shekhar. 2023. A neural CRF-based hierarchical approach for linear text seg- mentation. In Findings of the Association for Com- putational Linguistics: EACL...

    2023

  21. [31]

    Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka, and Isao Echizen. 2022. Ease: Entity-aware contrastive learning of sentence embed- ding. Preprint, arXiv:2205.04260

    2022 arXiv

  22. [32]

    Yixin Nie, Songhe Wang, and Mohit Bansal

  23. [33]

    How do we encode knowledge to use at scale in open, evolving, decentralised systems?

    Dr Jeff Pan. How do we encode knowledge to use at scale in open, evolving, decentralised systems?

  24. [34]

    Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, and Samuel R. Bowman. 2022. Quality: Question answering with long input texts, yes! Preprint, arXiv:2112.08608

    2022 arXiv

  25. [35]

    Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2024. Fine-tuning or retrieval? comparing knowledge injection in llms. Preprint, arXiv:2312.05934

    2024 arXiv

  26. [36]

    Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning

  27. [37]

    Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, and Tracy King. 2024. Augmenting KG Hierarchies Using Neural Transformers , page 298–303. Springer Nature Switzerland

    2024

  28. [38]

    Nils Reimers and Iryna Gurevych. 2019. Sentence- BERT: Sentence embeddings using Siamese BERT- networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP...

    2019

  29. [39]

    Lenka Tˇetková, Teresa Karen Scheidt, Maria Man- drup Fogh, Ellen Marie Gaunby Jørgensen, Finn Årup Nielsen, and Lars Kai Hansen. 2024. Knowl- edge graphs for empirical concept retrieval. Preprint, arXiv:2404.07008

    2024 arXiv

  30. [40]

    Preprint, arXiv:2401.18059

    Raptor: Recursive abstractive processing for tree-organized retrieval. Preprint, arXiv:2401.18059

    arXiv

  31. [41]

    Dense passage retrieval for open-domain question answering

    Sewon Min Patrick Lewis Ledell Wu Sergey Edunov Danqi Chen Wen-tau Yih Vladimir Karpukhin, Barlas Oguz. Dense passage retrieval for open-domain question answering

  32. [42]

    Amit Sheth, Swati Padhee, and Amelie Gyrard

  33. [43]

    Yuqi Wang, Boran Jiang, Yi Luo, Dawei He, Peng Cheng, and Liangcai Gao. 2024. Reasoning on efficient knowledge paths:knowledge graph guides large language model for domain question answering. Preprint, arXiv:2404.10384

    2024 arXiv

  34. [44]

    Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, and Mingchen Cai. 2024. Common sense enhanced knowledge-based recom- mendation with large language model. Preprint, arXiv:2403.18325

    2024 arXiv

  35. [45]

    McLachlan

    Cinzia Viroli and Geoffrey J. McLachlan

  36. [46]

    Shiyue Zhang, David Wan, and Mohit Bansal. 2023. Extractive is not faithful: An investigation of broad unfaithfulness problems in extractive summarization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers), page...

    2023

  37. [47]

    Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhen- gren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang, and Bin Cui. 2024. Retrieval-augmented generation for ai-generated con- tent: A survey. Preprint, arXiv:2402.19473. A Appendix Figure 2: Architecture of SKET...

    2024 arXiv

  38. [48]

    Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rong- bin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, and Guoyin Wang. 2023. Gpt-ner: Named entity recognition via large language models. Preprint, arXiv:2304.10428

    2023 arXiv

  39. [51]

    Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, and Zhaofeng Liu. 2024. Evaluation of retrieval-augmented generation: A survey. Preprint, arXiv:2405.07437

    2024 arXiv

  40. [2017]

    Preprint, arXiv:1711.06929

    Deep gaussian mixture models. Preprint, arXiv:1711.06929

    arXiv

  41. [2019]

    Preprint, arXiv:1909.08041

    Revealing the importance of semantic re- trieval for machine reading at scale. Preprint, arXiv:1909.08041

    1909 arXiv

  42. [2020]

    Preprint, arXiv:2003.03623

    Knowledge graphs and knowledge networks: The story in brief. Preprint, arXiv:2003.03623

    2003 arXiv

  43. [2021]

    ACM Computing Surveys, 54(4):1–37

    Knowledge graphs. ACM Computing Surveys, 54(4):1–37

  44. [2022]

    Preprint, arXiv:2112.04426

    Improving language models by retrieving from trillions of tokens. Preprint, arXiv:2112.04426

    arXiv

  45. [2024]

    Preprint, arXiv:2305.18703

    Domain specialization as the key to make large language models disruptive: A comprehensive survey. Preprint, arXiv:2305.18703

    arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.