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Paper Citation Record · LEDGER

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 3 inbound Pith citation observations for arXiv:2505.19819.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.19819 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:11:42.578894Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:58:08.181911Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T04:57:38.031024Z

Reference resolution

51 of 51 outbound references displayed

  • verified exact0
  • verified fuzzy37
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d3a5630f-efa8-46a8-914f-ac0177f038c5 · outbound

This paper cites FinTral: A family of GPT-4 level multimodal financial large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets FinTral: A family of GPT-4 level multimodal financial large language models

Reference 1

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raw_fallback, observed 2026-08-07T14:11:49.104361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:39.246647Z digest=sha256:7cf5af48b7ee02dc20266a656c4dadbd23047a562842db1003e8a744a2944013

Observation 6511fbbd-c3f1-46ee-b8ff-c79d0df21df9 · outbound

This paper cites Can GPT models be financial analysts? an evaluation of ChatGPT and GPT-4 on mock CFA exams.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Can GPT models be financial analysts? an evaluation of ChatGPT and GPT-4 on mock CFA exams

Reference 2

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raw_fallback, observed 2026-08-07T14:11:48.962148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:39.342654Z digest=sha256:263444c43577098c313ceb5f5e257b33020f0764dccc89d661f18633fa7d88cc

Observation b1394721-6235-4fa8-b73c-cb0ecf8386d1 · outbound

This paper cites Data-driven detection of subtype-specific differentially expressed genes.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Data-driven detection of subtype-specific differentially expressed genes

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:39.505600Z digest=sha256:b05d09580ffba9865b37e124fbe97040a2dc8da00ebd80a2974cd54e5a8594fb

Observation 48f708f6-7515-400e-8b80-c8d323f2c8fd · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:39.626787Z digest=sha256:311b0bab17c34a6292b109a4196339a8cead014bba826ced8490367270f2136e

Observation 2cd4e657-936d-432a-b872-bf51dbb331f9 · outbound

This paper cites Uncertainty quantification and interpretability for clinical trial approval prediction.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Uncertainty quantification and interpretability for clinical trial approval prediction

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:39.731931Z digest=sha256:b75ab8745146ee9ad1881e57e59f675b1e494321d6bb937117aea092c2668919

Observation ca91a882-17ef-4450-86a1-89dd5b064ac8 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Training Verifiers to Solve Math Word Problems

Reference 6

Resolution
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no resolver link, observed 2026-08-07T14:11:39.838185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:39.838185Z digest=sha256:3f0f5adc98727574a5c3066bd93daba455454a5fa1baf522a7b7d8cb7116365e

Observation 6d509754-befa-4dd1-bcec-e4053570fba9 · outbound

This paper cites QLoRA: Efficient finetuning of quantized LLMs.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets QLoRA: Efficient finetuning of quantized LLMs

Reference 7

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raw_fallback, observed 2026-08-07T14:11:48.500841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:39.988772Z digest=sha256:9c3af6b52000109ad37d7586533cd3d31e5c927b6ba96389c013b8e8dbc1b7f9

Observation 3cb6de15-a6d5-49fa-8ccd-7599685b8626 · outbound

This paper cites The Llama 3 herd of models, 2024.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets The Llama 3 herd of models, 2024

Reference 8

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raw_fallback, observed 2026-08-07T14:11:48.333186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.093645Z digest=sha256:6f31b0e336e7ae6e4b3fcffd29140314daa2c6a9ae1ecb010a6a5270a93a8e62

Observation e111d828-fd10-4c4c-950a-fc2157e51c7b · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 9

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no resolver link, observed 2026-08-07T14:11:40.196632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:40.196632Z digest=sha256:a65bb8972a00c589f6672f01c7b593ad6a4ff7f9cdd5324f51d254dcdc6d184d

Observation 750c4e1b-4650-4a53-b2d6-94fd2b57cf9f · outbound

This paper cites XBRL Agent: Lever- aging large language models for financial report analysis.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets XBRL Agent: Lever- aging large language models for financial report analysis

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.262847Z digest=sha256:4ddd4b69f6f9632f1d15ef38e4d7d78e0678539d75b0458157db7c696f18e349

Observation 36631d34-9473-4e9f-b194-384fe6f1e9ac · outbound

This paper cites Measuring Massive Multitask Language Understanding.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Measuring Massive Multitask Language Understanding

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:40.342470Z digest=sha256:06bf960f3b5930d6e80fe0bad07adc64026695d6fbd32584e81b348bf4e69b9d

Observation b5237299-29cd-43ae-abef-6536ea36b51f · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets LoRA: Low-rank adaptation of large language models

Reference 12

Resolution
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raw_fallback, observed 2026-08-07T14:11:47.922480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.376559Z digest=sha256:a22c1a4e1973d385529b219853b3ca01f60df2baa18f68996ae7b602ec3d225f

Observation 7c5d1168-833d-4c03-a7c4-458efba054c1 · outbound

This paper cites Fine-tuning transformers efficiently: A survey on LoRA and its impact.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Fine-tuning transformers efficiently: A survey on LoRA and its impact

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.495085Z digest=sha256:1171cfee055268aa36c4cf3e3a39f50427b83e4abf0888b574722bc0c5bff9e5

Observation d134c0ac-5e20-4273-99a7-3a7af56f917e · outbound

This paper cites GPT-4o System Card.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets GPT-4o System Card

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:40.571087Z digest=sha256:bc48dbb0993581dca70d8759d972eae30f90a74592a3c0c473904051acbe2655

Observation c83381c5-304b-4843-b517-abc84356098a · outbound

This paper cites FinanceBench: A new benchmark for financial question answering, 2023.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets FinanceBench: A new benchmark for financial question answering, 2023

Reference 15

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raw_fallback, observed 2026-08-07T14:11:47.585218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.660121Z digest=sha256:467fce257b188828cddce7bd9449b438043c313a3d637d6961ddca3856d65229

Observation 20882398-8fbb-4f1f-b422-3ddad6aa2c76 · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA, 2023.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA, 2023

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.736689Z digest=sha256:2cee969ea4c4143206300f05f6708c539a04124fe16e12e9eb65cbc97aa816fc

Observation 465c7dbb-adc0-4f40-b917-fdf1a7d4a91f · outbound

This paper cites Large language models in finance (finllms).

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Large language models in finance (finllms)

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:40.839952Z digest=sha256:d9a5dce639413771b8326f4b476892c5ddaed878b868bc767828fba53e57e6b9

Observation 729ee39c-20aa-4373-84f2-4f1810c0987c · outbound

This paper cites DeepSeek-V3 Technical Report.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets DeepSeek-V3 Technical Report

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:40.986821Z digest=sha256:323984e7534af2374d82b5e9486e8b727e7a539c9657d20289747bdd523aa4ac

Observation 05e585e6-7c6c-4f9b-957d-894c07c88510 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:41.062376Z digest=sha256:57a08a3c94991846b8dfcacc428ab7f6e888d982baff649508fc7949e04a0a2a

Observation 3258dce4-fdd1-426d-8d0b-d1045446c97e · outbound

This paper cites SocraticLM: Exploring socratic personalized teaching with large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets SocraticLM: Exploring socratic personalized teaching with large language models

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.106269Z digest=sha256:263b9fb5f7ce18783a0471dc36f356cf164552cbf1e3dc33d9ab447d4f74cd48

Observation 7ec16e2a-7dd7-47ee-b0c8-0aaaf433dfd3 · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets DoRA: Weight-decomposed low-rank adaptation

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.155754Z digest=sha256:e63f3976f90d4344c7b118f648d414c8fa871cf3a0d0f0cc8773b8f3725ecff5

Observation 262f86d3-3b4c-4226-ade7-df7ed785fb6a · outbound

This paper cites Data-centric FinGPT: De- mocratizing internet-scale data for financial large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Data-centric FinGPT: De- mocratizing internet-scale data for financial large language models

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.205574Z digest=sha256:e548ea6de49bc542544e15b7318e033f6b1689ff786a13f492ffca7b02d4f4a9

Observation 797c915c-5902-49e4-a8e6-14f1cb3ce5a3 · outbound

This paper cites Efficient Pretraining and Finetuning of Quantized LLMs with Low-Rank Structure.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Efficient Pretraining and Finetuning of Quantized LLMs with Low-Rank Structure

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.259661Z digest=sha256:df845db12421121a9b6cdf28bc4d8b5852cd499fe9b7bf584daa5d8e7ee4f624

Observation 8528b444-4eea-4975-b55f-275485581961 · outbound

This paper cites Zhu, Daochen Zha, J.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Zhu, Daochen Zha, J

Reference 24

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.302880Z digest=sha256:2efc76e7bf2e6b12c339800e47b597f769379f2ecbfeca0ee1eefd32ce805dbf

Observation 2e99b5b0-7d6f-4c47-8832-5e7a8ffa9eee · outbound

This paper cites FiNER: Financial numeric entity recognition for XBRL tagging.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets FiNER: Financial numeric entity recognition for XBRL tagging

Reference 25

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raw_fallback, observed 2026-08-07T14:11:46.106379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.345245Z digest=sha256:3cecafd609972c0c26b62e9ddad75613384fe430f4e20f9d4b9a31de6fc23e0e

Observation ddc349f9-36a6-467c-8833-9f75bc4f4cb2 · outbound

This paper cites COT: an effi- cient and accurate method for detecting marker genes among many subtypes.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets COT: an effi- cient and accurate method for detecting marker genes among many subtypes

Reference 26

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raw_fallback, observed 2026-08-07T14:11:45.894364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.386532Z digest=sha256:05635f811d4d00f6ebfb9e0c1ec26d551ea9681a356a9abefe1666da72c6c41c

Observation a389ca3d-567a-4584-8dc2-88f24eea476f · outbound

This paper cites Www’18 open challenge: Financial opinion mining and question answering.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Www’18 open challenge: Financial opinion mining and question answering

Reference 27

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raw_fallback, observed 2026-08-07T14:11:45.716392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.425924Z digest=sha256:eb2886521d7620166724d94aebc5538a3411856922ed1df259a44151050775d2

Observation a64fe48c-c6f1-4e46-bb27-00293e45f1f5 · outbound

This paper cites Good debt or bad debt: Detecting semantic orientations in economic texts, 2013.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Good debt or bad debt: Detecting semantic orientations in economic texts, 2013

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:41.478067Z digest=sha256:c07a2084ff986beb598a2fea25b7ddd24f76a40aa0bba69ae54c8cd35ad3cb80

Observation 3ba58098-dac2-4e2a-9196-3d033ddc885c · outbound

This paper cites A survey on lora of large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets A survey on lora of large language models

Reference 29

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raw_fallback, observed 2026-08-07T14:11:45.548991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.553667Z digest=sha256:9654be3fee32df844d6c380aa8c9b932d2ff95e0f08e4112fbcdb5613d0eb758

Observation 8a797ba5-a284-4735-809d-0213e596b3bb · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 30

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raw_fallback, observed 2026-08-07T14:11:45.345385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.596625Z digest=sha256:2aa2e4110125aa9506c7718492dce76ead6015a9ff9291d806ff5eda6b3bda7a

Observation 87201375-af03-4d12-a554-80c544b90602 · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Pissa: Principal singular values and singular vectors adaptation of large language models

Reference 31

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no resolver link, observed 2026-08-07T14:11:41.639875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:41.639875Z digest=sha256:38547686da29d9f779deb74e3208e9d4e5980626a1fd5234ec700c9bdd55544b

Observation 8bedec23-2503-4d87-9113-340ee3bbddac · outbound

This paper cites OpenAI API pricing.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets OpenAI API pricing

Reference 32

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raw_fallback, observed 2026-08-07T14:11:45.173845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.680307Z digest=sha256:a83547ce12a356a7cd5f347371ffcbcb8cc5fd761aab952df287d42dc87461a2

Observation 018c58fc-c2f6-414a-b8b6-db0bfe34b464 · outbound

This paper cites Abdur Rahman.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Abdur Rahman

Reference 33

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raw_fallback, observed 2026-08-07T14:11:44.972056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.736515Z digest=sha256:cc9d7001bcee0520439e622db306d44baf49ad3a293c513b5398deaa0c64507b

Observation 1ff13f1b-0a0f-4e76-b7a2-f9955444e9b0 · outbound

This paper cites An introduction to XBRL.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets An introduction to XBRL

Reference 34

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raw_fallback, observed 2026-08-07T14:11:44.771797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.788989Z digest=sha256:69fc2fd0a893af218a17695650f40334fca7826af0a0a5fc3d61fa990ffdf685

Observation 48ffc979-4fe7-4e46-b590-db2359fd8e47 · outbound

This paper cites Domain adaption of named entity recognition to support credit risk assessment.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Domain adaption of named entity recognition to support credit risk assessment

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:44.614579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.851905Z digest=sha256:482ed5e062c4c8b79b26b58a33da7233d908aa172cb5b03e8f8c08b815f90b46

Observation fc648b19-5170-46ca-b438-62e95669c8c7 · outbound

This paper cites Financial numeric extreme labelling: A dataset and bench- marking.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Financial numeric extreme labelling: A dataset and bench- marking

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:44.529191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.885848Z digest=sha256:2d0c2435bd5365e7f435e92c2bb887cd45a6bb257a650ff10da4f5c32e694500

Observation f9dd62ed-dca8-4ce5-a31f-bc16445b277c · outbound

This paper cites Impact of news on the commodity market: Dataset and results, 2020.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Impact of news on the commodity market: Dataset and results, 2020

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:44.420011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.928894Z digest=sha256:44d635773df05c51ec501176fd06ed831014b6312551a6757bbb8411af527d82

Observation 43c2ca13-61b0-41f2-8a97-8ca788ba77c6 · outbound

This paper cites Improving loRA in privacy-preserving federated learning.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Improving loRA in privacy-preserving federated learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:44.266373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:41.998039Z digest=sha256:9bd037b6d6e44562966c74dc8d17fda2e81703c840c7fd0ab11400cc537cf416

Observation f314dace-0224-417b-bfcf-cd56add068e1 · outbound

This paper cites Gemini: A family of highly capable multimodal models, 2024.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Gemini: A family of highly capable multimodal models, 2024

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:44.018211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.034108Z digest=sha256:82784e8dfaf6e6182367d8e3b8ec201a201fc01cf23cf4a78bb737fcb9870fda

Observation b3a6e000-7ef7-4a2c-a0c6-d86811c052a1 · outbound

This paper cites PrivateLoRA for efficient privacy preserving LLM, 2023.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets PrivateLoRA for efficient privacy preserving LLM, 2023

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:43.599924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.063844Z digest=sha256:9a1bf17ba5236335071184b1a5b7c122d2f78dd0bed83e644c72fcd979a0f75f

Observation a96eae73-3283-453f-9f6f-7c723133a59a · outbound

This paper cites TWIN-GPT: Digital twins for clinical trials via large language model.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets TWIN-GPT: Digital twins for clinical trials via large language model

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:43.403673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.103639Z digest=sha256:322f805e78fd1e280170d98e3eb9e0da6183e3c50869aa24853c9c0256ae78da

Observation 6a7734a0-f17d-4fb9-ade4-fbc60d005084 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets BloombergGPT: A Large Language Model for Finance

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:42.163647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:42.163647Z digest=sha256:b45ebc95a12dbb733b0cb6852ac62fb510fcdecf7d7792f105da80bcee6e195c

Observation 62792e65-e642-41f1-9c47-6f86257f3f44 · outbound

This paper cites Knowledge-infused legal wisdom: Navigating llm consultation through the lens of diagnostics and positive-unlabeled reinforcement learning.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Knowledge-infused legal wisdom: Navigating llm consultation through the lens of diagnostics and positive-unlabeled reinforcement learning

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:43.256240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.225646Z digest=sha256:09e3acbe5a124008bce56d3f43c6d9e3379eccb1752500a99568fd1572792593

Observation c289c73d-871b-4f28-9607-93aa22408e04 · outbound

This paper cites FinBen: An holistic financial benchmark for large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets FinBen: An holistic financial benchmark for large language models

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:43.107008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.269668Z digest=sha256:834663ee6d2b3f983a5bb201afcfb0eced2d1eedb22b671db1b916e7e8c01617

Observation 4822b7e9-62ad-4e96-b283-7f1fde6fa6c9 · outbound

This paper cites PIXIU: A comprehensive benchmark, instruction dataset and large language model for finance.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets PIXIU: A comprehensive benchmark, instruction dataset and large language model for finance

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:42.987643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.301833Z digest=sha256:03373fcca4cbfe126c4b936f8e7b089d5014f6ff935edef13dbae6fdf02e669a

Observation fdc1f753-00d5-4992-bd54-a05100e1fd82 · outbound

This paper cites Low-rank adaptation for foundation models: A comprehensive review, 2024.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Low-rank adaptation for foundation models: A comprehensive review, 2024

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:42.860668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.342434Z digest=sha256:f4680cf25cbb975c3d48773f46f3361d00b4ee310d2fc95bfa4ae5b74d471438

Observation 6479a3f8-dea3-4d78-a845-82562679214a · outbound

This paper cites Enhancing financial sentiment analysis via retrieval augmented large language models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Enhancing financial sentiment analysis via retrieval augmented large language models

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:11:42.755015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:11:42.381951Z digest=sha256:58e9f4088e325b308f0c15d0247bab7eb54e318b95b31577cea945723e109dd7

Observation 38d8a261-9e56-4be3-9428-6d3114d3568d · outbound

This paper cites Bertscore: Evaluating text generation with bert.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Bertscore: Evaluating text generation with bert

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:42.450931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:42.450931Z digest=sha256:0a7bb28bec6073002787b0dd7fc0dfa400b8cc7a0cdd0f10344aa032f4a63611

Observation 43921dde-16db-4461-8fe4-7c51f45f0cfd · outbound

This paper cites A Survey of Large Language Models.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets A Survey of Large Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:42.489605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:42.489605Z digest=sha256:f1983640882d580b3a14b9e31c9306af037daa7fb6d32a17e0ea20987f199902

Observation 8a94fc7d-8f65-4f21-b2fb-7be391047e26 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:42.535067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:42.535067Z digest=sha256:cb8630fa17fb990fa252f672452d2ffa608680d4474b97edcf56a0a892fea161

Observation 8a6d47fc-5f13-4139-bca7-bc28e7dd7c0b · outbound

This paper cites Large Language Models for Disease Diagnosis: A Scoping Review.

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Large Language Models for Disease Diagnosis: A Scoping Review

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:42.578894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:42.578894Z digest=sha256:a2de4d746ef3aa88b1e92a3f87ecb7b606af9c7df44c9872fcfa46970a22e64b

Pith citing papers

Observation a33054bd-a2e3-4a1e-9225-0ba9446bc606 · inbound

Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval cites this paper.

Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T22:32:43.903613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T22:31:06.866817Z digest=sha256:337dfc7eed79f428a13932ae3d956e315f4ee6c97eb2515de5f9b07c0ebf5da2

Observation 9596a0fa-7725-463d-ba0f-2406a44ce5a8 · inbound

EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents cites this paper.

EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-03T04:57:38.032370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T13:34:32.750618Z digest=sha256:44f6acf0699a5d10f54d3facd9363c8181e17b7424115faa4c32cdf957b1838f

Observation 2a653bc0-de73-4415-a241-44863d1ff648 · inbound

MiniCache: Reusable Program Caching with Small Model Interfaces for Efficient LLM Inference cites this paper.

MiniCache: Reusable Program Caching with Small Model Interfaces for Efficient LLM Inference FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-02T08:58:08.181911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T08:58:08.181911Z digest=sha256:2d3da837ff52bf90c1038d04c312992df45e21f0556f1b861844f947ad297d3d