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Paper Citation Record · LEDGER

Analysis on LLMs Performance for Code Summarization

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.17094.

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

pith.paper-citation-record.v1
2412.17094 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:50:08.066577Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74ddc12d-8245-4d9b-b638-8673d2e1532d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Analysis on LLMs Performance for Code Summarization Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=pdf_text observed=2026-08-11T05:50:07.945617Z digest=sha256:6f9e1225326bf8e411e027b3f12f806384f44fa0ad851646b3d8eccb4c7fb1a5

Observation ea9794dc-ee17-4fb9-9a0a-e9f627bdaee9 · outbound

This paper cites Gpt-4 technical report,(2023),.

Analysis on LLMs Performance for Code Summarization Gpt-4 technical report,(2023),

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.950125Z digest=sha256:41ab6838510c4029411f17330592a53f4dbc71b592adb1f0e90211584ba16e2a

Observation 89341afa-b7c1-465f-b5f9-5cb1e797f479 · outbound

This paper cites Few-shot training llms for project-specific code- summarization,.

Analysis on LLMs Performance for Code Summarization Few-shot training llms for project-specific code- summarization,

Reference 3

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source=pdf_text observed=2026-08-11T05:50:07.953528Z digest=sha256:5fbb93c54a1a8b0181a2949191f8fa3749b0591e0bce4d346e09a95b56637a3d

Observation 668b998e-23c2-414b-9e76-8580d2166916 · outbound

This paper cites Llama3modelcard,.

Analysis on LLMs Performance for Code Summarization Llama3modelcard,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.956953Z digest=sha256:f05c70b205d3ec64ab504c393cb413b66810a1d82e14f29383ada314c1739539

Observation b21965ea-baee-41b5-8829-2c1804837f52 · outbound

This paper cites code2seq: Generating Sequences from Structured Representations of Code.

Analysis on LLMs Performance for Code Summarization code2seq: Generating Sequences from Structured Representations of Code

Reference 5

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source=pdf_text observed=2026-08-11T05:50:07.960567Z digest=sha256:df4cade0118d338b1ef9df3326e69712c4ea9dcc8fbeccb375323b9d71018dd0

Observation 5ac542cd-7c15-475b-bc8e-7d42343eb6a6 · outbound

This paper cites A parallel corpus of Python functions and documentation strings for automated code documentation and code generation.

Analysis on LLMs Performance for Code Summarization A parallel corpus of Python functions and documentation strings for automated code documentation and code generation

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.964494Z digest=sha256:ef78600967195ecef037bee5a46a9706727e526487b9e61cd4eaea1ef107fc00

Observation e5909fb8-3b00-40eb-88f9-18d94358b9b8 · outbound

This paper cites GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization.

Analysis on LLMs Performance for Code Summarization GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization

Reference 7

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.968310Z digest=sha256:9774ba6e6d1d2e129a865971ce8a75fac01a4c49bce3d86041c0a4ffeb0a1bdd

Observation aa7b2409-d4e3-44d6-a505-4216ef1be13b · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Analysis on LLMs Performance for Code Summarization Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 8

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source=pdf_text observed=2026-08-11T05:50:07.971824Z digest=sha256:a230479cb21c8c762517829dffa0d68ef3b0c109997a3dfda5cc06f1f5fefaaf

Observation db26764d-9730-4773-b0fc-805cf6ee81eb · outbound

This paper cites Structured Neural Summarization.

Analysis on LLMs Performance for Code Summarization Structured Neural Summarization

Reference 9

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source=pdf_text observed=2026-08-11T05:50:07.975359Z digest=sha256:0ba3c2b6e29755e73515ffa28ab90f163823474a9b582372c9fed4e06b6de6cb

Observation 59551b56-8175-4572-bb80-7e72e0091c7f · outbound

This paper cites Code Structure Guided Transformer for Source Code Summarization.

Analysis on LLMs Performance for Code Summarization Code Structure Guided Transformer for Source Code Summarization

Reference 10

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local_arxiv, observed 2026-08-11T05:50:08.198657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.978730Z digest=sha256:b30f4efb308714a67a74126546639a1baeca9b8b28f48f97c8e01d89f4be3492

Observation ab072b7f-4b42-4b82-b010-d4ca2bea8fad · outbound

This paper cites M2ts: Multi-scale multi-modal approach based on trans- former for source code summarization,.

Analysis on LLMs Performance for Code Summarization M2ts: Multi-scale multi-modal approach based on trans- former for source code summarization,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.981968Z digest=sha256:eb442630e34f272900370e221474cc0bbee9616091244a156dde9f2d516b8b4a

Observation 311a94bf-470e-45c5-b1d3-b022b9fd8b0a · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

Analysis on LLMs Performance for Code Summarization GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 12

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source=pdf_text observed=2026-08-11T05:50:07.985661Z digest=sha256:e1e07fa6220d6e6f6987f9d0581f76a6f0daa3af663054a00879834fdcb2475a

Observation 4e724de9-1390-4c15-b383-8b66371ff842 · outbound

This paper cites Analyzing the performance of large language modelsoncodesummarization,.

Analysis on LLMs Performance for Code Summarization Analyzing the performance of large language modelsoncodesummarization,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.989218Z digest=sha256:8fbd053b447f4da1657f8c54eeb4e1d393c598d243caf979727e7d337d83aa09

Observation 298046f8-5583-4f99-bbac-ab0d5418793b · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

Analysis on LLMs Performance for Code Summarization Measuring Coding Challenge Competence With APPS

Reference 14

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source=pdf_text observed=2026-08-11T05:50:07.992821Z digest=sha256:f74515c83f0100bd3089fdb4cc6b50d456cb695652ca03ace8368f42bc5699b3

Observation 575196ff-49ef-4477-9aa8-4cb43b4f3b58 · outbound

This paper cites Deep code comment generation,.

Analysis on LLMs Performance for Code Summarization Deep code comment generation,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:07.996819Z digest=sha256:f8f76707f3c7e60969efd60feda3de6953e57d079a65ea6cadfc8b1d9a2f3d6f

Observation 3854f393-9d8c-4373-93ea-bffb9ebf5376 · outbound

This paper cites Summarizing source code with transferredapiknowledge,.

Analysis on LLMs Performance for Code Summarization Summarizing source code with transferredapiknowledge,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.000591Z digest=sha256:13d3058e60c332616e544f8440d43b345abbece5ae1b9c12b9e1d9490a6ac7b1

Observation 8066b834-0615-45ac-b671-0dfa95a8bce0 · outbound

This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

Analysis on LLMs Performance for Code Summarization CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 17

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source=pdf_text observed=2026-08-11T05:50:08.004115Z digest=sha256:006b6a02213778253702d0ea0437c2da89e1c523628a66430dbcf2d629a125af

Observation 456e59f9-52a8-44f2-8cbd-7ab34bceec8a · outbound

This paper cites Summarizing source code using a neural attention model,.

Analysis on LLMs Performance for Code Summarization Summarizing source code using a neural attention model,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.008171Z digest=sha256:4704ba681dd20b1f302fbcb94f766811b5bc421f703d61063c464d8e47638060

Observation 24e04c51-5324-468a-9084-495f21be72a8 · outbound

This paper cites Mistral 7B.

Analysis on LLMs Performance for Code Summarization Mistral 7B

Reference 19

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source=pdf_text observed=2026-08-11T05:50:08.012080Z digest=sha256:b34d5624cb2271e465dd53854eb6c77f4b4bd7dac323afe522aa0719912e93a3

Observation 89bd2816-fde2-482e-ad0d-1c551b575645 · outbound

This paper cites Improvedcodesummarization viaagraphneuralnetwork,.

Analysis on LLMs Performance for Code Summarization Improvedcodesummarization viaagraphneuralnetwork,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.016258Z digest=sha256:cf9cec8d3bee20a5969a4e15d981f0c590a1e59662db65d6dbf176c3c94349c3

Observation 94478b1e-6eea-4368-a33a-850f7afafa03 · outbound

This paper cites A neural model for generating natural languagesummariesofprogramsubroutines,.

Analysis on LLMs Performance for Code Summarization A neural model for generating natural languagesummariesofprogramsubroutines,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.020062Z digest=sha256:27c7f8c4fb6918f146df4ac5f8998899c246e83aced84b6c1458ae83de48bb97

Observation 8a295db7-36ff-49ec-912f-5198118c223d · outbound

This paper cites Retrieval-Augmented Generation for Code Summarization via Hybrid GNN.

Analysis on LLMs Performance for Code Summarization Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Reference 22

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source=pdf_text observed=2026-08-11T05:50:08.023574Z digest=sha256:13b88a44f11203caf5768bc265a341102ae2a536ee8b514b45ee2d3df5cb4cbf

Observation 777d7a3c-d540-42fb-9f47-1d652e7d22b4 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Analysis on LLMs Performance for Code Summarization CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 23

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source=pdf_text observed=2026-08-11T05:50:08.027100Z digest=sha256:e72a6631e330c0d2693375096d9f7a59a29bb38a278dc1fb629692fe42dfcbf8

Observation 5a1c5c1f-4b81-4bb2-a4fe-774e3706eb50 · outbound

This paper cites Codegen:Anopenlargelanguagemodelforcodewithmulti- turnprogramsynthesis,.

Analysis on LLMs Performance for Code Summarization Codegen:Anopenlargelanguagemodelforcodewithmulti- turnprogramsynthesis,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.030623Z digest=sha256:8a28da38008b29735dbe0e0fc580abd11f55a6d028c39d4878bb589429fb7dc7

Observation a9f93254-5033-4b31-8ab1-65cafbff3ff6 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Analysis on LLMs Performance for Code Summarization Code Llama: Open Foundation Models for Code

Reference 25

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source=pdf_text observed=2026-08-11T05:50:08.034012Z digest=sha256:d308cf7cb442c7796d99d1f9d72d6f0553ec9ef5240432945c04bde8aa81d47b

Observation f775a6d0-7930-40b0-aee9-5f3a8326f524 · outbound

This paper cites Au- tomaticsourcecodesummarizationwithextendedtree-lstm,.

Analysis on LLMs Performance for Code Summarization Au- tomaticsourcecodesummarizationwithextendedtree-lstm,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.037529Z digest=sha256:57840fb9d99f246ec8fd31b50f643a1ef7c03863a9bf3a482c132e37d1ba3e4a

Observation f91aa0c3-7678-46c1-bc64-2776a887b434 · outbound

This paper cites Sequencetosequencelearningwithneu- ralnetworks,.

Analysis on LLMs Performance for Code Summarization Sequencetosequencelearningwithneu- ralnetworks,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.040865Z digest=sha256:c468da37e6836b0a1ef19f51b9a196bf29341501d70d4c7f97cf16f7c20a2633

Observation d621579c-9b9b-4e3f-9220-a98cbd3f1521 · outbound

This paper cites Ast-trans: Code summarization with efficient tree-structured attention,.

Analysis on LLMs Performance for Code Summarization Ast-trans: Code summarization with efficient tree-structured attention,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.044163Z digest=sha256:dabfcc33be25466e104f3b3a85dd48eefb2f8fb87792861cf3d8e73b84e9f8bc

Observation 38c38901-65c5-41e5-b9f6-4d82d908a848 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Analysis on LLMs Performance for Code Summarization Gemini: A Family of Highly Capable Multimodal Models

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:08.048039Z digest=sha256:46097e71363b7a53682b5573c46c67cab6c4c917fd77a513f2a14f745bde17c2

Observation 59ed357a-985e-4ce7-a0b4-9dd1e1aac951 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Analysis on LLMs Performance for Code Summarization Gemma: Open Models Based on Gemini Research and Technology

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:08.053155Z digest=sha256:1244e34c3d3eb27dd921d7e9d87374c0dae0fb3dbb8fac1d0b50d9af09436715

Observation 6e9feecc-daea-4f92-9786-9af8f7924748 · outbound

This paper cites Attentionisallyouneed,.

Analysis on LLMs Performance for Code Summarization Attentionisallyouneed,

Reference 31

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raw_fallback, observed 2026-08-11T05:50:08.366167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.056789Z digest=sha256:ad2a4e3ef34d9e549daf38ccdbb4f89c06de812546ad39ba041919718b1063d8

Observation bfd22a0b-e80c-475c-846d-f95a94711c86 · outbound

This paper cites Improving automatic source code summa- rizationviadeepreinforcementlearning,.

Analysis on LLMs Performance for Code Summarization Improving automatic source code summa- rizationviadeepreinforcementlearning,

Reference 32

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raw_fallback, observed 2026-08-11T05:50:08.355134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.060191Z digest=sha256:e2bdec21a737f82c03b10e43676482c6cf87ecd8251e10468dafda6f877b108c

Observation 708e712f-bff0-49e2-a2ac-f5dc8a0596ba · outbound

This paper cites Asurveyofautomaticsourcecodesumma- rization,.

Analysis on LLMs Performance for Code Summarization Asurveyofautomaticsourcecodesumma- rization,

Reference 33

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raw_fallback, observed 2026-08-11T05:50:08.343757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.063353Z digest=sha256:cdbbd11987fee5777d7c2581c99414a61f8c9d1367379bddfdeadd497f404f51

Observation a3ea10eb-b5bb-46fd-bed7-3951b950ab1e · outbound

This paper cites Retrieval-basedneuralsource codesummarization,.

Analysis on LLMs Performance for Code Summarization Retrieval-basedneuralsource codesummarization,

Reference 34

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raw_fallback, observed 2026-08-11T05:50:08.333912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T05:50:08.066577Z digest=sha256:a067642eae544e58d8b28c24801014ef5d6ade911fe80dfe798af0a42aa69811

Pith citing papers

No inbound Pith citation observations are available.