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Paper Citation Record · LEDGER

WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2211.00083.

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

pith.paper-citation-record.v1
2211.00083 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:45:27.039391Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:37:24.373395Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e448fb97-e911-423f-a72f-936167906b2d · inbound

Bridging Language Models and Financial Analysis cites this paper.

Bridging Language Models and Financial Analysis WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 86

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:12:20.672084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T01:08:58.528533Z digest=sha256:f6295f45046f9f05a2b2992dff24911b4f79d0ff3422ac5abe617be85955e617

Observation 75fbcc30-3cc1-4b2d-91ab-310c13400896 · inbound

MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market cites this paper.

MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-22T21:52:10.831785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-22T21:50:24.666622Z digest=sha256:830e42235ef89c5b3050550f6fb7b9928d5642b3ff0c707b79f5db4d6b13fb71

Observation ce57edaf-9138-4a1a-9e8a-22fb1fcea8b4 · inbound

Domain Specific Benchmarks for Evaluating Multimodal Large Language Models cites this paper.

Domain Specific Benchmarks for Evaluating Multimodal Large Language Models WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-07T00:39:41.985661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:39:41.985661Z digest=sha256:f18634ed2fef22f5d9b5eea1f09470d4a2b471658a228ce69e9d268d3c732ba1

Observation 325e99a7-1c22-418d-a853-c0e536d07a3c · inbound

CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model cites this paper.

CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:52.858650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:40:52.858650Z digest=sha256:88cb9d1229d46a25c273d8a6556b125f4d751eff6161f1a8c26239aa13de5ece

Observation 9e6287b6-52fe-4e2e-ad5a-024229d2e6e8 · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T22:56:31.776768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:56:31.776768Z digest=sha256:147119128ea7adecb2166b0260b3023c842a7f364013cf167fbc467bbd67dcb5

Observation b3d281d5-acff-468b-92bf-ce72bfc5a2c5 · inbound

VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations cites this paper.

VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T10:45:27.039391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:45:27.039391Z digest=sha256:2851f98fbc613e374f6bf2a3d45d5a0143631e46d2a0a72911b3de16f0735060

Observation 53e9afb8-9bf2-4422-bf8d-f1099d16d972 · inbound

MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model cites this paper.

MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:31:07.823347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T03:21:32.522414Z digest=sha256:26fb948a1745629353a04f04c51c15752779b1f36a5bca101c4e0baa3c446d8b

Observation 5a57463d-0f4b-4095-83f8-3af69754852b · inbound

Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain cites this paper.

Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T02:16:15.795139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T02:15:53.024591Z digest=sha256:5b350a11c487b1e9d96bc63659e43f1f016d129e453558c60c69d4521e4f03bc

Observation 04f1524b-6a2e-4ec1-a2f3-8de937b5be31 · inbound

IPO-Mine: A Toolkit and Dataset for Section-Structured Analysis of Long, Multimodal IPO Documents cites this paper.

IPO-Mine: A Toolkit and Dataset for Section-Structured Analysis of Long, Multimodal IPO Documents WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-06-29T12:33:24.478713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T12:28:29.456480Z digest=sha256:e4637fd4c2b1dd6a4980d5c9ea8058b8e755ac6324975bb599e712e2a10d013a

Observation b6504b2d-83b4-4750-99da-a017547ae80d · inbound

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset cites this paper.

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:03:13.743436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T08:03:05.904864Z digest=sha256:23ab97e745f14d317f98b0dda1a3209af1b05fd5a4be359e23767f75d64b0d67

Observation 43b20d9c-8d32-4e92-9cb1-6eaffcd4e76b · inbound

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions cites this paper.

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:37:24.375161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T19:42:28.510902Z digest=sha256:94bb7d12a0197c0431ef3460faa79bbf54cc43d060669f5444fde45af1389128