Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance

Ji-jun Park; Soo-joon Choi

REVIEW 4 major objections 4 minor 34 references

Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read Reframing entity extraction as JSON generation hits F1 0.945 in finance

desk verdict The paper's claimed SOTA F1 on CCKS 2019 rests on an unnamed base LLM, making the result unreproducible; the evaluation is thorough but the central number is not verifiable. read the letter →

arxiv 2504.14633 v1 pith:W5XRLRJY submitted 2025-04-20 cs.CL

Soo-joon Choi , Ji-jun Park This is my paper · ORCID

classification cs.CL

keywords financialevententityextractiongenerativeLLMparameter-efficientfine-tuningLoRACCKS2019structuredoutputgenerationsequencelabelingJSONspan

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 claims that financial event entity extraction—identifying companies, people, dates, amounts, and locations tied to events like acquisitions and bankruptcies—can be reformulated as a text-to-JSON generation task. By fine-tuning a large pre-trained language model with LoRA to output entity text and character offsets directly, the method reaches an F1 of 0.945 on the CCKS 2019 test set, above the previous state-of-the-art sequence-labeling model (0.934). The authors argue the generative approach handles long-range dependencies, overlapping entities, and dense instances better than token-level tagging, and support this with per-event, per-entity, complexity, span-accuracy, and human-evaluation analyses. If correct, the result suggests that generative LLMs with parameter-efficient fine-tuning are a strong default for domain-specific structured extraction.

What carries the argument

The load-bearing piece of the method is a LoRA-adapted generative LLM trained to maximize the likelihood of a JSON target sequence given a prompt with an instruction, an event type, and the raw text. The target JSON includes each entity's surface text and its start and end character indices in the original input, so the model must learn to map token positions to character offsets. Because LoRA freezes the pre-trained weights and trains only small low-rank adapters, the method adapts a large model with far fewer trainable parameters than full fine-tuning.

What would settle it

Train the same LoRA prompt-to-JSON setup with a publicly named Chinese-capable base LLM and evaluate on the official CCKS 2019 test set; if F1 falls at or below 0.934, the method's edge over sebertNets disappears. Repeating with two different base models would show whether the gain is tied to the specific unnamed model.

Watch

Extended reading notes

Core claim

The central discovery is that treating entity extraction as a structured-output generation problem outperforms token-level sequence labeling. On the CCKS 2019 Financial Event Entity Extraction test set, a LoRA-fine-tuned generative LLM that outputs a JSON list of entities with exact character offsets reaches precision 0.948, recall 0.942, and F1 0.945, surpassing the strongest sequence-labeling baseline sebertNets (0.925, 0.943, 0.934). Across all examined event types, entity types, and complexity levels the generative model stays ahead, and its margin widens on instances with five or more entities (0.905 vs 0.875). The error analysis and human evaluation reinforce the same conclusion: more exact span matches, fewer spurious entities, and higher perceived extraction quality.

Load-bearing premise

The claimed F1 of 0.945 depends on an unnamed pre-trained language model whose tokenizer and context length must correctly align character offsets in Chinese financial text; if that model is replaced, the result may not reproduce.

Editorial extensions

If this is right

The same prompt-to-JSON recipe can be lifted to other information-extraction tasks: relation extraction, event argument extraction, and nested entity recognition.
Parameter-efficient fine-tuning makes the approach feasible with modest compute, so the paradigm is accessible for teams working on domain-specific IE.
Because the output is already structured JSON, downstream pipelines like knowledge-graph construction and alerting systems can consume the extraction directly without span-conversion post-processing.
The improved handling of dense instances (5+ entities) suggests the method is especially valuable for verbose financial documents such as merger filings and regulatory releases.
Reported error patterns—fewer spurious entities but occasional boundary errors—point to span-boundary calibration as the next natural improvement target.

Reading between the lines

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

The paper never names its base LLM; if the result transfers across base models, the method is robust, but if it hinges on a specific tokenizer's Chinese character-offset behavior, the headline F1 may not generalize. A natural next experiment is running the identical LoRA setup on several open-weight Chinese-capable base models and comparing F1.
The generative paradigm trades some inference-time latency and decoding cost for tagging simplicity; in real-time trading settings, a distilled or speculative-decoding variant could be needed to make the approach practical.
One testable extension the authors leave implicit is using the same JSON-generation objective to jointly extract entities across multiple event types in a single pass, which could reduce error propagation compared to per-event prompts.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things you should know. The method is the now-standard recipe: take a pretrained LLM, LoRA fine-tune it to emit JSON entity lists with character spans, evaluate on CCKS 2019. The result is a claimed F1 of 0.945 vs 0.934 for sebertNets. The catch: the base LLM is never identified. Section 3.2 says 'a large, pre-trained Transformer-based language model'; Section 4.1 repeats that without a name, checkpoint, tokenizer, or context length. That makes the headline number unreproducible, and the 0.011 gap is small enough to plausibly be model-choice noise.

What's good: the paper does a fairly thorough evaluation. It reports precision/recall/F1, breaks performance down by event type, entity type, instance complexity, analyzes span/error types, and adds a human evaluation. The LoRA setup is described in enough detail—learning rate, batch size, epochs, rank are listed as hyperparameters, though not the actual values. The writing is clear about the task reformulation.

Soft spots beyond the missing model: no variance or significance testing—a single test-set run. Baselines are not rerun; they're taken from the sebertNets paper. And the text claims 'improvements in both Precision and Recall,' but Table 1 shows Recall 0.942 vs 0.943, a slight drop. The paper acknowledges 'a slight trade-off' in the same paragraph, so it's internally inconsistent in wording, not necessarily in data. Still, it doesn't inspire confidence.

The paper also cites no prior generative NER work (e.g., other LLM-based extraction methods), so it doesn't position itself against the closest alternatives.

Who gets value: someone wanting a template for PEFT-based extraction on a Chinese financial benchmark might find the breakdown useful, but as a research claim, the SOTA assertion is not verifiable. I'd recommend a serious editor send it to review only if the authors can identify the base model and share code/hyperparameters; otherwise, desk reject. The empirical core is plausible, but the omission is load-bearing.

Referee Report

4 major / 4 minor

Summary. This paper proposes a generative approach to financial event entity extraction: a pretrained LLM is fine-tuned with LoRA to map a prompt containing the input text and event type to a JSON object listing each entity with its character start and end indices. The method is evaluated on the CCKS 2019 Financial Event Entity Extraction dataset against sequence-labeling baselines, reporting an F1 score of 0.945 on the test set, which the authors claim is a new state of the art over sebertNets (0.934). The paper also presents per-event-type, per-entity-type, complexity-based, span-error, and human evaluations.

Significance. If the headline result were reproducible, the paper would provide a useful demonstration that generative LLMs with PEFT can perform span-level financial event entity extraction competitively with specialized sequence-labeling architectures. The task formulation is clear, the choice of a standard benchmark is appropriate, and the additional error-type and human evaluations are a positive aspect. However, the contribution is currently not verifiable because the base model is unnamed, no uncertainty estimates are provided, baseline numbers are not re-run, and several tables are internally inconsistent. As it stands, the paper does not substantiate its state-of-the-art claim.

major comments (4)

[Sections 3.2 and 4.1] The pre-trained LLM is never identified; the text refers only to "a large, pre-trained Transformer-based language model." The method's central result depends on this model's tokenizer and its ability to emit character offsets for Chinese text, so without the model name, checkpoint, tokenizer, context length, and decoding settings, the reported F1 of 0.945 cannot be reproduced or verified.
[Section 4.2 and Table 1] The text states the method achieved "the highest performance across all evaluated metrics" and "improvements in both Precision and Recall," but Table 1 shows Recall 0.942 for the proposed method versus 0.943 for sebertNets, a decrease. The numerical claim is therefore internally inconsistent; the authors should correct the prose and report uncertainty (e.g., multiple seeds, confidence intervals, or a significance test), especially since the F1 gap is only 0.011.
[Section 4.1, Baselines] The baseline results are quoted from the published sebertNets paper [6] rather than re-run under the same experimental conditions. To support a state-of-the-art claim, the authors need to either re-run the baselines with the same evaluation script and data split or provide evidence that the published numbers are directly comparable.
[Section 4.7 and Table 5] The span-error analysis is numerically inconsistent with Table 1. For sebertNets, Table 5 reports Exact Match (of Gold) as 93.4% and Missing (of Gold) as 4.6%, implying a recall near 93.4%, whereas Table 1 reports Recall 0.943; for the proposed method, Spurious (of Predicted) 5.0% implies a precision of 95.0%, not 94.8%. The definitions and computations behind Table 5 must be clarified and reconciled with the headline metrics.

minor comments (4)

[Section 4.1] "Hyperparameters suchables as" appears to be a typo; it should read "such as."
[Tables 2-4] Tables 2-4 present per-event-type, per-entity-type, and complexity-stratified F1 scores without reporting the number of instances in each stratum or any uncertainty, which limits their interpretability.
[Section 4.8] The human evaluation does not report inter-annotator agreement (e.g., Cohen's kappa) or a significance test for the 4.25 vs. 3.82 difference, so the strength of that conclusion is unclear.
[Related Work] References [32] and [34] discuss healthcare and higher-education chatbot applications, not financial LLMs; the related-work section would benefit from either removing these citations or explicitly explaining their relevance to finance.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports an empirical benchmark evaluation with no fitted-to-test parameters and no load-bearing self-citations.

full rationale

The paper's central claim is an experimental F1 result on the official CCKS 2019 test set, not a derived quantity. The method defines a generative task (Section 3.1) in which the target output Y* is built from gold annotations and the model is trained with a standard negative log-likelihood objective (Equations 1-3). There is no equation in which the reported F1 score is defined in terms of the model's training objective or in which the benchmark metric is constructed from the method's own output format. The claimed state-of-the-art result (Table 1: F1 0.945) is a comparison against externally published baselines (SEBERTNets, sebertNets) evaluated on the official held-out test split. Hyperparameters are stated to be selected on the validation set (Section 4.1: 'Hyperparameters ... were determined based on performance on the validation set'), so no test-set fitting is evidenced. The paper does not rely on self-citations: the author names do not overlap with the cited prior work, and no uniqueness theorem or prior claim by the same authors is invoked to force the method choice. The unnamed base LLM is a reproducibility weakness rather than a circularity: it does not make the result an input to itself. The minor textual inconsistency around Recall (Section 4.2 says 'improvements in both Precision and Recall' while Table 1 shows Recall 0.942 vs. 0.943, later described as 'a slight trade-off') is a numerical reporting nuance, not a circular step. No circularity pattern from the enumerated list is present, so the appropriate finding is no significant circularity.

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

The paper introduces no new theoretical constructs. Its central claim rests entirely on empirical evaluation, which depends on unspecified hyperparameters, an unnamed base model, and the assumption that the reported baselines are trustworthy.

free parameters (4)

Learning rate = Not reported
Required for LoRA fine-tuning, but the paper gives no value, so the result cannot be reproduced.
Batch size = Not reported
A standard training hyperparameter, unspecified in Section 4.1.
Number of training epochs = Not reported
Training duration directly affects the final model, but no epoch count is given.
LoRA rank = Not reported
Section 3.3 describes LoRA but does not state the rank r used in experiments.

assumptions (3)

domain assumption The CCKS 2019 dataset annotations and the official evaluation metric are used exactly as described.
The paper relies on the public benchmark's ground truth and scoring procedure, but does not specify the preprocessing steps that convert original annotations into spans.
domain assumption The baseline scores for SEBERTNets and sebertNets are accurate and directly comparable.
The paper does not state whether these baselines were re-implemented or numbers were copied from reference [6], so comparability is assumed.
domain assumption The unnamed base LLM supports the tokenization and span indexing required for the dataset's language.
Section 3.2 and Section 4.1 never name the model, yet the method's success depends on its tokenizer and character-offset generation capability in Chinese.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance." pith.science (2026). https://pith.science/paper/W5XRLRJY

@misc{pith2026250414633,
  author       = {Pith},
  title        = {Pith review of: Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W5XRLRJY}},
  note         = {Machine review of arXiv:2504.14633}
}

read the original abstract

Financial event entity extraction is a crucial task for analyzing market dynamics and building financial knowledge graphs, yet it presents significant challenges due to the specialized language and complex structures in financial texts. Traditional approaches often rely on sequence labeling models, which can struggle with long-range dependencies and the inherent complexity of extracting multiple, potentially overlapping entities. Motivated by the advanced language understanding and generative capabilities of Large Language Models (LLMs), we propose a novel method that reframes financial event entity extraction as a text-to-structured-output generation task. Our approach involves fine-tuning a pre-trained LLM using Parameter-Efficient Fine-Tuning (PEFT) to directly generate a structured representation, such as a JSON object, containing the extracted entities and their precise character spans from the input text. We evaluate our method on the challenging CCKS 2019 Financial Event Entity Extraction dataset, comparing its performance against strong sequence labeling baselines, including SEBERTNets and sebertNets. Experimental results demonstrate that our generative LLM method achieves a new state-of-the-art F1 score on this benchmark, significantly outperforming previous methods. Through detailed quantitative analysis across event types, entity types, and instance complexity, as well as human evaluation, we show that our approach is more effective at handling the nuances of financial text and extracting high-quality entities. This work validates the potential of applying generative LLMs directly to complex, domain-specific information extraction tasks requiring structured output.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 15 canonical work pages

[6]

SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field

He, C., Zhu, X., Le, Y., Liu, Y., Yin, J.: Sebertnets: Seque nce enhanced BERT networks for event entity extraction tasks oriented to the ﬁnance ﬁeld. CoRR abs/2401.11408 (2024). https://doi.org/10.48550/ARXIV.2401.11408, https://doi.org/10.48550/arXiv.2401.11408

work page Pith review arXiv doi:10.48550/arxiv.2401.11408 2024
[1]

Applied Sciences 12(19), 9691 (2022)

Yang, Y., Wu, Z., Yang, Y., Lian, S., Guo, F., Wang, Z.: A sur vey of information extraction based on deep learning. Applied Sciences 12(19), 9691 (2022)

work page 2022
[2]

Elhammadi, S.: Financial knowledge graph construction. Ph.D. thesis, University of British Columbia (2020)

work page 2020
[3]

Findings of the Association for Computational Linguistics: ACL-IJCNLP 20 21 (2021)

Zhou, Y., Geng, X., Shen, T., Pei, J., Zhang, W., Jiang, D.: Modeling event- pair relations in external knowledge graphs for script reas oning. Findings of the Association for Computational Linguistics: ACL-IJCNLP 20 21 (2021)

2021
[4]

In: 2023 IEEE International Conference on Data Mining (ICDM)

Mei, H., Li, J., Liang, Z., Zheng, G., Shi, B., Wei, H.: Unce rtainty-aware traﬃc prediction under missing data. In: 2023 IEEE International Conference on Data Mining (ICDM). pp. 1223–1228. IEEE (2023)

work page 2023
[5]

arXiv preprint arXiv:2304.107 22 (2023)

Mei, H., Li, J., Shi, B., Wei, H.: Reinforcement learning a pproaches for traﬃc signal control under missing data. arXiv preprint arXiv:2304.107 22 (2023)

work page 2023
[7]

arXiv preprint arXiv :2003.03875 (2020)

Han, X., Wang, Z., Zhang, J., Wen, Q., Li, W., Tang, B., Wang , Q., Feng, Z., Zhang, Y., Lu, Y., et al.: Overview of the ccks 2019 knowledge graph evaluation track: entity, relation, event and qa. arXiv preprint arXiv :2003.03875 (2020)

arXiv 2020
[8]

In : Proceedings of the AAAI Conference on Artiﬁcial Intelligence

Zhou, Y., Shen, T., Geng, X., Tao, C., Shen, J., Long, G., Xu , C., Jiang, D.: Fine-grained distillation for long document retrieval. In : Proceedings of the AAAI Conference on Artiﬁcial Intelligence. vol. 38, pp. 19732–1 9740 (2024)

2024

Show all 34 references

[9]

I n: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V

Petruzzellis, F., Testolin, A., Sperduti, A.: Assessing the emergent sym- bolic reasoning abilities of llama large language models. I n: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds.) Artiﬁc ial Neural Net- works and Machine Learning - ICANN 2024 - 33rd Internat...

2024
[10]

In: Koyejo, S ., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A

Wang, Z., Li, M., Xu, R., Zhou, L., Lei, J., Lin, X., Wang, S ., Yang, Z., Zhu, C., Hoiem, D., Chang, S., Bansal, M., Ji, H.: Language models wit h image descriptors are strong few-shot video-language learners. In: Koyejo, S ., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., ...

2022
[11]

In: Proceedings of the AAAI Confer ence on Artiﬁcial Intelligence

Li, J., Zhang, D., Wang, X., Hao, Z., Lei, J., Tan, Q., Zhou , C., Liu, W., Yang, Y., Xiong, X., et al.: Chemvlm: Exploring the power of multim odal large language models in chemistry area. In: Proceedings of the AAAI Confer ence on Artiﬁcial Intelligence. vol. 39, pp. 415–423 (2025)

2025
[12]

, Fleming, S.L., Pf- eﬀer, M.A., Fries, J.A., Shah, N.H.: The shaky foundations o f clinical founda- tion models: A survey of large language models and foundatio n models for emrs

Wornow, M., Xu, Y., Thapa, R., Patel, B.S., Steinberg, E. , Fleming, S.L., Pf- eﬀer, M.A., Fries, J.A., Shah, N.H.: The shaky foundations o f clinical founda- tion models: A survey of large language models and foundatio n models for emrs. CoRR abs/2303.12961 (2023). https://do...

2023 doi arXiv
[13]

In: The Thir- teenth International Conference on Learning Representati ons (2025), https://openreview.net/forum?id=N1vYivuSKq

Zhou, Y., Shen, J., Cheng, Y.: Weak to strong generalizat ion for large language models with multi-capabilities. In: The Thir- teenth International Conference on Learning Representati ons (2025), https://openreview.net/forum?id=N1vYivuSKq

2025
[14]

In: Findings of the Association for Comput ational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11- 16, 2024

Zhou, Y., Li, X., Wang, Q., Shen, J.: Visual in-context le arning for large vision- language models. In: Findings of the Association for Comput ational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11- 16, 2024. pp. 15890– 15902. Association for Computat...

2024
[15]

Zhou, Y., Zhang, J., Chen, G., Shen, J., Cheng, Y.: Less is more: Vision repre- sentation compression for eﬃcient video generation with la rge language models (2024)

2024
[16]

D., Duke, T., Soraker, J., Zevenbergen, B., Prabhakaran, V., Diaz, M., Hutchinson, B

Thoppilan, R., Freitas, D.D., Hall, J., Shazeer, N., Kul shreshtha, A., Cheng, H., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H.S ., Ghafouri, A., Mene- gali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., Chen, D., Xu, Y., Chen, Z., Roberts, A., Bosma, M....

2022 arXiv
[17]

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra , G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., Schuh, P. , Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prab- hakaran, V., Reif, E., Du, N., Hutchinson, B., Po...

2023
[18]

arXiv preprint arXiv:2307.0928 8 (2023)

Touvron, H., Martin, L., Stone, K., Albert, P., Almahair i, A., Babaei, Y., Bash- lykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llam a 2: Open foundation and ﬁne-tuned chat models. arXiv preprint arXiv:2307.0928 8 (2023)

2023
[19]

CoRR abs/2203.15556 (2022)

Hoﬀmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Ruther- ford, E., de Las Casas, D., Hendricks, L.A., Welbl, J., Clark , A., Hen- nigan, T., Noland, E., Millican, K., van den Driessche, G., D amoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Rae, J.W., ...

2022 doi arXiv
[20]

Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J. , Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olss on, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson , E., Perez, E., Kerr, J., Mueller, J., Ladish, J., Landau, J...

2022 doi arXiv
[21]

R., Ma, F., Cui, L.: Emobench-m: Benchmarking emotional intelligence for mult imodal large language models

Hu, H., Zhou, Y., You, L., Xu, H., Wang, Q., Lian, Z., Yu, F. R., Ma, F., Cui, L.: Emobench-m: Benchmarking emotional intelligence for mult imodal large language models. arXiv preprint arXiv:2502.04424 (2025)

2025 arXiv
[22]

In: Findings of the Associ ation for Computational Linguistics: ACL 2023

Zhou, Y., Shen, T., Geng, X., Tao, C., Xu, C., Long, G., Jia o, B., Jiang, D.: Towards robust ranker for text retrieval. In: Findings of the Associ ation for Computational Linguistics: ACL 2023. pp. 5387–5401 (2023)

2023
[23]

CoRR abs/2402.02315 (2024)

Lee, J., Stevens, N., Han, S.C., Song, M.: A sur- vey of large language models in ﬁnance (ﬁnllms). CoRR abs/2402.02315 (2024). https://doi.org/10.48550/ARXIV.2402.02315, https://doi.org/10.48550/arXiv.2402.02315

2024 doi arXiv
[24]

CoRR abs/2307.10485 (2023)

Liu, X., Wang, G., Zha, D.: Fingpt: Democratizing intern et- scale data for ﬁnancial large language models. CoRR abs/2307.10485 (2023). https://doi.org/10.48550/ARXIV.2307.10485, https://doi.org/10.48550/arXiv.2307.10485

2023 doi arXiv
[25]

In: 2 024 IEEE Conference on Artiﬁcial Intelligence (CAI)

Du, K., Xing, F., Mao, R., Cambria, E.: An evaluation of re asoning capabilities of large language models in ﬁnancial sentiment analysis. In: 2 024 IEEE Conference on Artiﬁcial Intelligence (CAI). pp. 189–194. IEEE (2024)

2024
[26]

Contemporary Accounting R esearch 40(2), 806– 841 (2023)

Huang, A.H., Wang, H., Yang, Y.: Finbert: A large languag e model for extracting information from ﬁnancial text. Contemporary Accounting R esearch 40(2), 806– 841 (2023)

2023
[27]

In: Proceedings of the fourth ACM international conference on AI in ﬁnance

Li, Y., Wang, S., Ding, H., Chen, H.: Large language model s in ﬁnance: A survey. In: Proceedings of the fourth ACM international conference on AI in ﬁnance. pp. 374–382 (2023)

2023
[28]

arXiv preprint arXiv:2406.11903 (2024)

Nie, Y., Kong, Y., Dong, X., Mulvey, J.M., Poor, H.V., Wen , Q., Zohren, S.: A survey of large language models for ﬁnancial applications: Progress, prospects and challenges. arXiv preprint arXiv:2406.11903 (2024)

2024 arXiv
[29]

arXiv preprint arXiv:1908.10063 (2019) 18 S

Araci, D.: Finbert: Financial sentiment analysis with p re-trained language models. arXiv preprint arXiv:1908.10063 (2019) 18 S. Choi et al

2019 arXiv
[30]

arXiv preprint arXiv:2401.02982 (2024)

Liu, S., Zhao, S., Jia, C., Zhuang, X., Long, Z., Zhou, J., Zhou, A., Lan, M., Wu, Q., Yang, C.: Findabench: Benchmarking ﬁnancial data analy sis ability of large language models. arXiv preprint arXiv:2401.02982 (2024)

2024 arXiv
[31]

In: 2023 IEEE International Confere nce on Big Data (Big- Data)

Vanetik, N., Podkaminer, E., Litvak, M.: Summarizing ﬁn ancial reports with posi- tional language model. In: 2023 IEEE International Confere nce on Big Data (Big- Data). pp. 2877–2883. IEEE (2023)

2023
[32]

In: Informatics

Nazi, Z.A., Peng, W.: Large language models in healthcar e and medical domain: A review. In: Informatics. vol. 11, p. 57. MDPI (2024)

2024
[33]

arXiv preprint arXiv:2501.0137 7 (2025)

Zhou, Y., Song, L., Shen, J.: Training medical large visi on-language models with abnormal-aware feedback. arXiv preprint arXiv:2501.0137 7 (2025)

2025
[34]

A vailable at SSRN 4548263 (2023)

Mvondo, G.F.N., Niu, B., Eivazinezhad, S.: Exploring th e ethical use of llm chat- bots in higher education. A vailable at SSRN 4548263 (2023)

2023

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [6]

SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field

He, C., Zhu, X., Le, Y., Liu, Y., Yin, J.: Sebertnets: Seque nce enhanced BERT networks for event entity extraction tasks oriented to the ﬁnance ﬁeld. CoRR abs/2401.11408 (2024). https://doi.org/10.48550/ARXIV.2401.11408, https://doi.org/10.48550/arXiv.2401.11408

work page Pith review arXiv doi:10.48550/arxiv.2401.11408 2024

[2] [1]

Applied Sciences 12(19), 9691 (2022)

Yang, Y., Wu, Z., Yang, Y., Lian, S., Guo, F., Wang, Z.: A sur vey of information extraction based on deep learning. Applied Sciences 12(19), 9691 (2022)

work page 2022

[3] [2]

Elhammadi, S.: Financial knowledge graph construction. Ph.D. thesis, University of British Columbia (2020)

work page 2020

[4] [3]

Findings of the Association for Computational Linguistics: ACL-IJCNLP 20 21 (2021)

Zhou, Y., Geng, X., Shen, T., Pei, J., Zhang, W., Jiang, D.: Modeling event- pair relations in external knowledge graphs for script reas oning. Findings of the Association for Computational Linguistics: ACL-IJCNLP 20 21 (2021)

2021

[5] [4]

In: 2023 IEEE International Conference on Data Mining (ICDM)

Mei, H., Li, J., Liang, Z., Zheng, G., Shi, B., Wei, H.: Unce rtainty-aware traﬃc prediction under missing data. In: 2023 IEEE International Conference on Data Mining (ICDM). pp. 1223–1228. IEEE (2023)

work page 2023

[6] [5]

arXiv preprint arXiv:2304.107 22 (2023)

Mei, H., Li, J., Shi, B., Wei, H.: Reinforcement learning a pproaches for traﬃc signal control under missing data. arXiv preprint arXiv:2304.107 22 (2023)

work page 2023

[7] [7]

arXiv preprint arXiv :2003.03875 (2020)

Han, X., Wang, Z., Zhang, J., Wen, Q., Li, W., Tang, B., Wang , Q., Feng, Z., Zhang, Y., Lu, Y., et al.: Overview of the ccks 2019 knowledge graph evaluation track: entity, relation, event and qa. arXiv preprint arXiv :2003.03875 (2020)

arXiv 2020

[8] [8]

In : Proceedings of the AAAI Conference on Artiﬁcial Intelligence

Zhou, Y., Shen, T., Geng, X., Tao, C., Shen, J., Long, G., Xu , C., Jiang, D.: Fine-grained distillation for long document retrieval. In : Proceedings of the AAAI Conference on Artiﬁcial Intelligence. vol. 38, pp. 19732–1 9740 (2024)

2024