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Paper Citation Record · LEDGER

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models

As of 7 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2508.21377.

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

pith.paper-citation-record.v1
2508.21377 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:22:57.852780Z

measured 14 of 14 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c79f0e9b-af0c-4670-a1e6-e1d33fcf51aa · outbound

This paper cites Attention is All You Need,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Attention is All You Need,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:59.229360Z

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=2026-08-05T14:22:56.209211Z digest=sha256:55ef864f22c47e09c4dacf93741b3f7125f127c239b8364cde19d5a855cd2be6

Observation 26668f33-391f-467f-888a-f39b5dc4973d · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely- Gated Mixture-of-Experts Layer,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Outrageously Large Neural Networks: The Sparsely- Gated Mixture-of-Experts Layer,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:59.056962Z

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=2026-08-05T14:22:56.300088Z digest=sha256:97a797535e5e3a652957dc8fc41c30ceff11b0b6e32417b1d3776c2c6fa11dd5

Observation 2334a054-fc59-4f86-bd8e-5a11c18b9770 · outbound

This paper cites Language Models are Few-Shot Learners,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Language Models are Few-Shot Learners,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.900837Z

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=2026-08-05T14:22:56.432654Z digest=sha256:219c2dfd99e616f6a997975ce33c2ec33423b7a2e1d389cd493b75e5a3731168

Observation a353b32f-a9d7-4fbd-8d8a-43e73fcfdecb · outbound

This paper cites Training Compute-Optimal Large Language Models.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Training Compute-Optimal Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T14:22:56.624508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:56.624508Z digest=sha256:539c4689240d58af16e592eee20661f4fbd020fa45776cd050c22b0b51df6d0e

Observation 5f25851f-02cd-448e-a791-9be3cf8900a7 · outbound

This paper cites Challenges and Applications of Large Language Models.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Challenges and Applications of Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T14:22:56.777525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:56.777525Z digest=sha256:f9a9bd0e8498dcc032dfa028a353d614e3abea1c498f942bf6ab0fbb7dde3912

Observation 018411bc-5547-44df-9266-687dbcfde823 · outbound

This paper cites Performance of GPT-4o and DeepSeek-R1 in the Polish Infectious Diseases Specialty Exam,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Performance of GPT-4o and DeepSeek-R1 in the Polish Infectious Diseases Specialty Exam,

Reference 6

Resolution
verified exact
doi, observed 2026-08-05T14:22:58.044503Z

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=2026-08-05T14:22:56.972200Z digest=sha256:794fc296cec66ae06161301f5501ce8e35b6b1941b64dc06dd98ba317aa4c391

Observation 034e734c-be34-4cfc-a616-47c368f9ba0b · outbound

This paper cites Training Language Models to Follow Instructions with Human Feedback,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Training Language Models to Follow Instructions with Human Feedback,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.768325Z

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=2026-08-05T14:22:57.198842Z digest=sha256:ac73fafe17bb5f1a70da115aeaa303b905f75d0b931127618f2814db9ea4fe2e

Observation 0bcc53be-dce6-48ad-bff5-87927965e619 · outbound

This paper cites A Watermark for Large Language Models,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models A Watermark for Large Language Models,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.649908Z

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=2026-08-05T14:22:57.282393Z digest=sha256:065710eea29ae5bc0c2bfacd3d7a3a4cecb8df45cb5c73a707f92aad6b224a7c

Observation 302708e9-09cd-4ad0-98c4-11531f3d3af3 · outbound

This paper cites DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.497893Z

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=2026-08-05T14:22:57.384059Z digest=sha256:81bdd4d86d1ef013dc68f940429a1b58188b750ec772fa4802d0e0aca2c92b5f

Observation 64e008ee-a90f-43df-aa6b-8dccbbbebb5b · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.366590Z

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=2026-08-05T14:22:57.471644Z digest=sha256:09d98eb796bfb323ad4cd05c94667ce0d57e1c979f19ffc9fd19756f4f7fc543

Observation 1f986525-646e-4e73-b83f-8c54e9c68c01 · outbound

This paper cites DeepSeek-V3 Technical Report.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models DeepSeek-V3 Technical Report

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T14:22:57.578440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:57.578440Z digest=sha256:ce3f868446df6bdffee3aa5ed03ee20091cb4b2e9e4f73705d8ffc0614950171

Observation 50070188-e8fd-42bf-a0a6-5774cbe25913 · outbound

This paper cites GPT-4 Technical Report.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models GPT-4 Technical Report

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T14:22:57.673201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:57.673201Z digest=sha256:5d1115cb49f9348dd77c64baf5481fbb157df2012d604d636a241498573dee7e

Observation 30493e3d-3690-4b18-8ce8-46382fd1f5da · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Self-Refine: Iterative Refinement with Self-Feedback

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-05T14:22:57.780571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:57.780571Z digest=sha256:2a43b89313ac8493a4243af1d0eb0fc60ee949d9e4e380479636046a53500d43

Observation 01f1209c-f505-473d-86c2-0671b2ad3c62 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation,.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:22:58.245658Z

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=2026-08-05T14:22:57.852780Z digest=sha256:7759ff4ebec584b42340858b1a82f3d49117214fe8ba8b2d7268d86f9450d546

Pith citing papers

No inbound Pith citation observations are available.