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REVIEW 3 major objections 4 minor 1 cited by

Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper argues that rewriting event schemas with few-shot examples makes schema retrieval substantially more accurate, and that a retrieval-then-extraction pipeline built on the paraphrased pool improves event extraction across domains.

desk verdict Schema paraphrasing clearly helps retrieval, but the reported end-to-end gains rest on a metric that ignores false-positive retrievals and a missing raw-schema baseline. read the letter →

arxiv 2505.08690 v1 pith:JTVJQCQM submitted 2025-05-13 cs.CL

Sheng Liang , Hang Lv , Zhihao Wen , Yaxiong Wu , Yongyue Zhang , Hao Wang , Yong Liu This is my paper · ORCID
classification cs.CL
keywords eventextractionschemaretrievalretrieval-augmentedgenerationparaphrasinglargelanguagemodelsMD-SEEcross-lingualbenchmark
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

Event extraction in practice requires choosing the right schema from hundreds of candidates before extracting anything, yet current systems either hard-code schemas or stuff all of them into the prompt. This paper proposes Adaptive Schema-aware Event Extraction (ASEE), which paraphrases each schema with few-shot training examples, retrieves the top-k paraphrased schemas for a query, and then extracts events with an LLM fine-tuned to follow those schemas. The paper constructs MD-SEE, a 300-schema, 12-dataset benchmark spanning domains, languages, and query lengths, and reports that paraphrased schemas improve retrieval Recall@10 across all retrievers tested, with BGE-M3 rising from 0.61 to 0.78 on MD-SEE. The paper's claim is that the bottleneck in schema-conditioned event extraction is the terse wording of raw schemas, and that rewriting them into fuller language is a cheap, transferable fix.

What carries the argument

The load-bearing object is the paraphrased schema pool S. For each raw schema s in S0, the paper uses training samples Ds that instantiate s as few-shot demonstrations and prompts a frozen LLM to rewrite s with fuller argument descriptions; the outputs form S. A retriever ψ, chosen from BM25, BGE-M3, E5, GTE, LLM-Embedder, or reranker variants, scores a query q against S and returns the top-k schemas. A supervised fine-tuned LLM θ then generates argument values from q and the retrieved schemas. The modified E2E-F1 metric given in Eq. 5 is also load-bearing: it averages extraction F1 over ground-truth schemas that were retrieved, assigns zero to gold schemas that were missed, and ignores retrieved schemas that are not in the gold set.

What would settle it

Compute an end-to-end score that penalizes retrieved-but-irrelevant schemas, for example by including false-positive schemas in the denominator or subtracting a precision term, on MD-SEE with BGE-M3 and Llama-3.1-8B. If the advantage of paraphrased schemas over raw schemas shrinks or reverses under that metric, the practical value of the pipeline depends on the cost of false positives.

Watch

Extended reading notes

Core claim

The central claim is that schema paraphrasing, using a frozen LLM to rewrite each raw schema with a few training examples as demonstrations, creates a schema pool whose entries align with real query language, making retrieval of the correct schema much easier. On MD-SEE, paraphrased schemas beat raw schemas for every one of the seven retrievers at Recall@10, Recall@20, and Recall@50; for instance, BGE-M3 reaches 0.78 Recall@10 on paraphrased schemas versus 0.61 on raw schemas. The paper then shows that a retrieve-then-extract system, where the extractor is an SFT-tuned open-weight LLM and only top-k schemas enter the prompt, achieves higher end-to-end F1 than a pipeline using raw schemas, with larger and fine-tuned extractors compounding the gain. In the paper's framing, the same two-stage recipe carries across news, finance, cybersecurity, biomedical, and legal domains as well as English-Chinese cross-lingual pairs.

Load-bearing premise

The end-to-end evaluation assumes that retrieving extra schemas that are not ground truth is harmless, because the score ignores them entirely and only rewards finding the correct ones.

Editorial extensions

If this is right

  • With paraphrased schemas, even the lexical retriever BM25 becomes usable, jumping from 0.33 to 0.58 Recall@10 on MD-SEE, which lowers the entry cost for retrieval in low-resource deployments.
  • The pipeline avoids context-window overflow by feeding only top-k schemas to the extractor, so the approach scales to hundreds of candidate schemas without prompt-compression tricks.
  • Schema consolidation plus a greedy maximum-independent-set step reduces duplicate schemas, so a 300-schema pool still covers 12 datasets without redundant entries.
  • The ASEE recipe with BGE-M3 as retriever and Llama-3.1-8B with supervised fine-tuning gives the best E2E-F1 on MD-SEE, meaning retrieval quality and extractor capacity both contribute to the gain.
  • Because the paraphrasing step is data-driven and language-agnostic, ASEE generalizes to Chinese queries with English schemas and English queries with Chinese schemas, suggesting multilingual deployment without extra annotation.

Reading between the lines

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

  • A testable extension is to measure precision-aware end-to-end accuracy: if every retrieved schema costs compute or confuses downstream users, the reported E2E-F1 may overstate the practical gain, since the metric ignores false-positive retrievals.
  • The paraphrasing step could serve as a general schema-normalization layer for relation extraction and named entity recognition, not just event extraction, because the same few-shot-rewrite-with-training-samples recipe applies to any structured schema.
  • A held-out paraphrase experiment would separate surface mimicry from semantic alignment: if retrieval is high only for paraphrases produced from the same training split, the gain may reflect lexical overlap rather than better schema understanding.
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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

3 major / 4 minor

Summary. The paper proposes ASEE, an adaptive schema-aware event extraction framework that combines schema paraphrasing with retrieval-augmented generation. Given a query, ASEE first paraphrases raw event schemas into a richer schema pool using few-shot training examples, then retrieves the top-k relevant schemas from a pool of hundreds, and finally extracts event arguments using an LLM guided by those retrieved schemas. To evaluate the joint task of schema selection and extraction, the authors construct MD-SEE, a benchmark that consolidates 12 existing datasets across domains, languages, and query lengths. Experiments report improved schema retrieval Recall@K with paraphrased schemas and an end-to-end E2E-F1 score on MD-SEE and individual datasets, claiming significant accuracy improvements over raw-schema pipelines. The paper also provides schema-aware extraction comparisons for several open-source LLMs and GPT-4-turbo.

Significance. If the claims hold, ASEE would be a useful practical contribution: it addresses the real-world need to select schemas from a large candidate pool before extraction, and MD-SEE provides a benchmark for the joint schema-matching and extraction setting. The paper also ships extensive empirical comparisons and a reproducible benchmark construction pipeline. The main scientific interest is the schema-paraphrasing idea, which appears to substantially help retrieval in Tables 1 and 2. However, the end-to-end evaluation is weakened by the choice of metric, which ignores retrieval precision, and by the absence of a raw-schema end-to-end baseline on MD-SEE. These issues affect the central claim that ASEE 'significantly improves' event extraction in the joint setting, so the current evidence is not yet convincing.

major comments (3)
  1. [Section 5.1.3, Eq. (5)] The E2E-F1 metric ignores all retrieved schemas that are not in the ground-truth set S_q. Consequently, a retriever that returns the entire 300-schema pool receives the same credit for each ground-truth schema as a perfectly precise retriever, and retrieval precision does not affect the reported score. Under this metric E2E-F1 is a recall-weighted extraction quality measure, not a joint retrieval-and-extraction accuracy measure. Because the paper's central end-to-end claim (Section 5.5, Abstract) is that schema paraphrasing improves the combined retrieval-and-extraction pipeline, the metric must penalize false-positive retrievals. Please re-evaluate with a metric that counts false-positive retrievals (for example, by scoring extractions made under unrequested schemas as false positives, or by using a joint F1 over all retrieved schemas).
  2. [Section 5.5, Table 4 ] The MD-SEE end-to-end evaluation compares different retrievers and extraction models, but every row uses the paraphrased schema pool; there is no raw-schema end-to-end baseline on MD-SEE. Thus Table 4 cannot by itself support the claim that schema paraphrasing improves end-to-end extraction. The per-dataset tables in Appendix B do provide Raw versus Paraph. comparisons, but those results are not uniformly favorable to paraphrasing: for example, in Table 7 (CrudeOilNews) several retrievers perform equal or slightly worse with Paraph. (BM25 0.07 vs 0.08; BGE-M3 0.07 vs 0.08; E5-LV2 equal at 0.08). Please provide the raw-schema end-to-end comparison on MD-SEE and discuss the per-dataset variability, which the current narrative glosses over.
  3. [Section 4.2.3 vs Table 6] The text states that MD-SEE contains 12,817 training samples, 1,775 development samples, and 7,686 test samples, but Table 6 reports a development size of 800 in the total row, and the per-source development counts sum to 800. This inconsistency makes the exact split of the benchmark ambiguous. Please correct the numbers and ensure the text, tables, and any released data agree.
minor comments (4)
  1. [Section 1, paragraph 2] The phrase 'we developed theMulti-DimensionalSchema-awareEventExtraction (MD-SEE) benchmark' contains a doubled 'we developed the'; please rewrite for clarity.
  2. [Section 5.1.2] The description of the schema-aware extraction F1 metric does not specify the matching granularity (e.g., exact string, token-level, or span-level). Specifying this is important for reproducibility, especially for multilingual and document-level queries.
  3. [Section 4.2.1 and Appendix A.3] The schema consolidation uses an ad-hoc cosine similarity threshold of 0.85 and a greedy independent-set heuristic; no sensitivity analysis is reported. Since the composition of the schema pool directly affects all subsequent retrieval and extraction results, an ablation on this threshold would strengthen the paper.
  4. [Section 5.5] The phrasing 'significantly improving the accuracy of event extraction' in the Abstract and Section 5.5 is not supported by any statistical significance tests. The recorded differences (e.g., BGE-M3 E2E-F1 0.62 with Llama-3.2-3B vs 0.46 for BM25) may be meaningful, but at least a paired significance test should be reported given the large test set.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the ASEE pipeline's fitted components use training data only, and the reported gains are measured on held-out test queries.

full rationale

The derivation chain is self-contained. Equation 1 generates paraphrased schemas from training-set few-shot examples using a frozen LLM; Equation 2 retrieves with pretrained or unsupervised retrievers; Equation 3 extracts with an LLM that is either zero-shot or supervised fine-tuned on D_train (Equation 4). No parameter is fitted to test labels, and the retrieval and extraction comparisons are evaluated on held-out queries, so the central claims are not forced by construction. The main evaluative weaknesses are not circularity: the E2E-F1 in Equation 5 ignores false-positive retrieved schemas, so it rewards recall-weighted extraction quality rather than true joint retrieval-and-extraction precision, and BGE-M3 is used both to consolidate the schema pool and as a retriever, which may create a benchmark-construction bias. These are reporting or metric-design concerns, not reductions of a prediction to its input. There is also no load-bearing self-citation chain: the cited SEE definition and IEPILE/GENEVA/MAVEN resources are external. Therefore, no circular step is exhibited.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The paper's central claim relies on several modeling choices (cosine threshold, filtering cutoffs, unspecified top-k) and on domain assumptions about LLM paraphrasing fidelity, embedding similarity validity, and cross-dataset annotation compatibility. No new theoretical entities are introduced; the MD-SEE benchmark is a constructed dataset, not an invented entity in the scientific sense.

free parameters (4)
  • Schema consolidation cosine threshold = 0.85
    Schemas with BGE-M3 cosine similarity above 0.85 are considered duplicates and one is removed (Section 4.2.1, Appendix A.3); the threshold is chosen without ablation.
  • Maximum event labels per instance = 15
    Instances with more than 15 extracted event labels are filtered out in MAVEN-Arg (Appendix A.2), a hand-set cutoff that may remove the most complex samples.
  • Top-k for schema retrieval (end-to-end) = not stated
    The end-to-end pipeline retrieves top-k schemas but k is never specified in Section 5; Recall@10/20/50 are reported for retrieval only.
  • Few-shot demonstrations per schema for paraphrasing = not stated
    Equation 1 uses subset D_s of training data as few-shot examples; the number of examples is unspecified, making paraphrasing hard to reproduce.
assumptions (3)
  • domain assumption LLM paraphrasing with few-shot examples produces semantically faithful, retrieval-friendly schema descriptions.
    Section 3.2.1 assumes paraphrased schemas are more semantically clear without evaluating whether paraphrases introduce errors.
  • domain assumption BGE-M3 embedding similarity is a valid measure of schema relatedness for both consolidation (threshold 0.85) and retrieval.
    Used in Section 4.2.1 and as a retriever in Section 5; no validation that the embedding space aligns with event-type semantics.
  • domain assumption Ground-truth event annotations in the 12 constituent datasets are correct and mutually compatible after schema merging.
    MD-SEE merges schemas across datasets (Section 4.2.1); annotation inconsistencies between datasets are not analyzed.

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Pith. "Pith review of Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation." pith.science (2026). https://pith.science/paper/JTVJQCQM

@misc{pith2026250508690,
  author       = {Pith},
  title        = {Pith review of: Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JTVJQCQM}},
  note         = {Machine review of arXiv:2505.08690}
}
read the original abstract

Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in real-world scenarios requires two key steps: selecting appropriate schemas from hundreds of candidates and executing the extraction process. Existing research exhibits two critical gaps: (1) the rigid schema fixation in existing pipeline systems, and (2) the absence of benchmarks for evaluating joint schema matching and extraction. Although large language models (LLMs) offer potential solutions, their schema hallucination tendencies and context window limitations pose challenges for practical deployment. In response, we propose Adaptive Schema-aware Event Extraction (ASEE), a novel paradigm combining schema paraphrasing with schema retrieval-augmented generation. ASEE adeptly retrieves paraphrased schemas and accurately generates targeted structures. To facilitate rigorous evaluation, we construct the Multi-Dimensional Schema-aware Event Extraction (MD-SEE) benchmark, which systematically consolidates 12 datasets across diverse domains, complexity levels, and language settings. Extensive evaluations on MD-SEE show that our proposed ASEE demonstrates strong adaptability across various scenarios, significantly improving the accuracy of event extraction.

Figures

Figures reproduced from arXiv: 2505.08690 by the authors.

Figure 1
Figure 1. An example of Adaptive Schema-aware Event [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The architecture of ASEE comprises two primary components: Schema Paraphrasing (SP) and Schema [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Data Statistics of MD-SEE. • Diverse Event Complexity: Accommodates both single-event and multi-event extraction sce￾narios, testing the system’s adaptability in handling complex event structures. • Multiple Language Settings: Encompasses English-only, Chinese-only, and cross-lingual ex￾traction subsets, highlighting proficiency in multi￾lingual and cross-lingual information extraction. 4.2.1 Schema Consolidation To… view at source ↗

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

33 extracted references · 6 canonical work pages · cited by 1 Pith paper

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  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 capitalize " " * FUNCT...

  3. [3]

    Marah Abdin, Sam Ade Jacobs, and Ammar Ahmad Awan. 2024. https://api.semanticscholar.org/CorpusID:269293048 Phi-3 technical report: A highly capable language model locally on your phone . ArXiv, abs/2404.14219

    arXiv 2024

  4. [4]

    D Ashok and ZC Lipton. 2023. Promptner: Prompting for named entity recognition. arxiv 2023. arXiv preprint arXiv:2305.15444

    arXiv 2023

  5. [5]

    Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. https://arxiv.org/abs/2402.03216 Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation . Preprint, arXiv:2402.03216

    arXiv 2024

  6. [6]

    Abhimanyu Dubey, Abhinav Jauhri, and Abhinav Pandey. 2024. https://api.semanticscholar.org/CorpusID:271571434 The llama 3 herd of models . ArXiv, abs/2407.21783

    arXiv 2024

  7. [7]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997

    arXiv 2023

  8. [8]

    Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, and Huajun Chen. 2024. 2024.acl-short.13 IEP ile: Unearthing large scale schema-conditioned information extraction corpus . pages 127--146, Bangkok, Thailand

    work page 2024

Show all 33 references
  1. [9]

    Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yutao Zeng, Wenxuan Liu, Xiang Li, Pan Yang, Long Bai, Jiafeng Guo, et al. 2023. Retrieval-augmented code generation for universal information extraction. arXiv preprint arXiv:2311.02962

    2023 arXiv

  2. [10]

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2025. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Info...

    2025

  3. [11]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas ...

    2023 arXiv

  4. [12]

    Viet Dac Lai. 2022. Event extraction: A survey. arXiv preprint arXiv:2210.03419

    2022 arXiv

  5. [13]

    Meisin Lee, Lay-Ki Soon, Eu Gene Siew, and Ly Fie Sugianto. 2022. 2022.lrec-1.49 C rude O il N ews: An annotated crude oil news corpus for event extraction . pages 465--479, Marseille, France. European Language Resources Association

    2022

  6. [14]

    Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023. https://api.semanticscholar.org/CorpusID:260682258 Towards general text embeddings with multi-stage contrastive learning . ArXiv, abs/2308.03281

    2023 arXiv

  7. [15]

    Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2022. Unified structure generation for universal information extraction. arXiv preprint arXiv:2203.12277

    2022 arXiv

  8. [16]

    Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2023. A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435

    2023 arXiv

  9. [17]

    Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, and Nanyun Peng. 2023. https://doi.org/10.18653/v1/2023.acl-long.203 GENEVA : Benchmarking generalizability for event argument extraction with hundreds of event types and argument roles . pages 3664--3686, Toronto, Canada

    2023 doi

  10. [18]

    Robertson and Hugo Zaragoza

    Stephen E. Robertson and Hugo Zaragoza. 2009. https://doi.org/10.1561/1500000019 The probabilistic relevance framework: BM25 and beyond . Found. Trends Inf. Retr., 3(4):333--389

    2009 doi

  11. [19]

    Fatemeh Shiri, Van Nguyen, Farhad Moghimifar, John Yoo, Gholamreza Haffari, and Yuan-Fang Li. 2024. https://arxiv.org/abs/2406.01045 Decompose, enrich, and extract! schema-aware event extraction using llms . Preprint, arXiv:2406.01045

    2024 arXiv

  12. [20]

    MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, and Juanzi Li. 2022. https://doi.org/10.18653/v1/2022.naacl-main.291 D oc EE : A large-scale and fine-grained benchmark for document-level event extraction . pages 3970--3982, Seattle, Un...

    2022 doi

  13. [21]

    Hayakawa, Junko Kikuta, Keigo Shimoji, Katsuhiro Sano, Koji Kamagata, Atsushi Nakanishi, and Shigeki Aoki

    Akihiko Wada, Toshiaki Akashi, George Shih, Akifumi Hagiwara, Mitsuo Nishizawa, Yayoi K. Hayakawa, Junko Kikuta, Keigo Shimoji, Katsuhiro Sano, Koji Kamagata, Atsushi Nakanishi, and Shigeki Aoki. 2024. https://api.semanticscholar.org/CorpusID:271285349 Optimizing gpt-4 turbo d...

    2024

  14. [22]

    Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2022 a . Deepstruct: Pretraining of language models for structure prediction. arXiv preprint arXiv:2205.10475

    2022 arXiv

  15. [23]

    Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 b . https://api.semanticscholar.org/CorpusID:254366618 Text embeddings by weakly-supervised contrastive pre-training . ArXiv, abs/2212.03533

    2022 arXiv

  16. [24]

    Qing Wang, Kang Zhou, Qiao Qiao, Yuepei Li, and Qi Li. 2023 a . Improving unsupervised relation extraction by augmenting diverse sentence pairs

    2023

  17. [25]

    X Wang, W Zhou, C Zu, H Xia, T Chen, Y Zhang, R Zheng, J Ye, Q Zhang, T Gui, et al. 2023 b . Instructuie: Multi-task instruction tuning for unified information extraction. arxiv 2023. arXiv preprint arXiv:2304.08085

    2023 arXiv

  18. [26]

    Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, and Juanzi Li. 2024 a . 2024.acl-long.224 MAVEN - ARG : Completing the puzzle of all-in-one event understanding dataset with event argument annotati...

    2024

  19. [27]

    Xindi Wang, Mahsa Salmani, Parsa Omidi, Xiangyu Ren, Mehdi Rezagholizadeh, and Armaghan Eshaghi. 2024 b . Beyond the limits: A survey of techniques to extend the context length in large language models. arXiv preprint arXiv:2402.02244

    2024 arXiv

  20. [28]

    Xinglin Xiao, Yijie Wang, Nan Xu, Yuqi Wang, Hanxuan Yang, Minzheng Wang, Yin Luo, Lei Wang, Wenji Mao, and Daniel Zeng. 2023. https://api.semanticscholar.org/CorpusID:266551409 Yayi-uie: A chat-enhanced instruction tuning framework for universal information extraction . ArXiv...

    2023 arXiv

  21. [29]

    Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, and Enhong Chen. 2023. Large language models for generative information extraction: A survey. arXiv preprint arXiv:2312.17617

    2023 arXiv

  22. [30]

    An Yang, Baosong Yang, and Binyuan Hui. 2024. https://api.semanticscholar.org/CorpusID:271212307 Qwen2 technical report . ArXiv, abs/2407.10671

    2024 arXiv

  23. [31]

    Junjie Ye, Nuo Xu, Yikun Wang, Jie Zhou, Qi Zhang, Tao Gui, and Xuanjing Huang. 2024. Llm-da: Data augmentation via large language models for few-shot named entity recognition. arXiv preprint arXiv:2402.14568

    2024 arXiv

  24. [32]

    Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, and Jian-Yun Nie. 2024. https://doi.org/10.18653/v1/2024.acl-long.194 A multi-task embedder for retrieval augmented LLM s . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1...

    2024 doi

  25. [33]

    Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2023. Universalner: Targeted distillation from large language models for open named entity recognition. arXiv preprint arXiv:2308.03279

    2023 arXiv

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