Paper Citation Record · LEDGER

Data-efficient LLM Fine-tuning for Code Generation

As of 23 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 2 inbound Pith citation observations for arXiv:2504.12687.

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

pith.paper-citation-record.v1
2504.12687 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:29:51.874378Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:36:26.513190Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:21:11.100740Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy5
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac34a435-cef3-49c4-a178-279b65f97fd2 · outbound

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

Data-efficient LLM Fine-tuning for Code Generation Code Llama: Open Foundation Models for Code

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.721363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57a98bca-771e-4fa4-855f-b18bffe2fbef · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Data-efficient LLM Fine-tuning for Code Generation DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.725858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 228ce2ef-be42-4520-ac80-14e290564557 · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

Data-efficient LLM Fine-tuning for Code Generation StarCoder 2 and The Stack v2: The Next Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.730390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39018f29-0944-4faf-bfc4-2742e480789d · outbound

This paper cites StarCoder: may the source be with you!.

Data-efficient LLM Fine-tuning for Code Generation StarCoder: may the source be with you!

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.735124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e412c66-7b3e-4a1e-997e-9afde0c0013f · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

Data-efficient LLM Fine-tuning for Code Generation When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.740384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a3c9bba-adb1-46dc-9e2d-eed0b0f3a0fe · outbound

This paper cites Code alpaca: An instruction-following llama model for code generation,.

Data-efficient LLM Fine-tuning for Code Generation Code alpaca: An instruction-following llama model for code generation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.745903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12abfb2d-6b4b-41bf-b320-26b93e289c16 · outbound

This paper cites Wizardcoder: Empowering code large language models with evol-instruct,.

Data-efficient LLM Fine-tuning for Code Generation Wizardcoder: Empowering code large language models with evol-instruct,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.750623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cab103d-debb-44e5-b2fa-83f141f358ed · outbound

This paper cites Magicoder: Empowering Code Generation with OSS-Instruct.

Data-efficient LLM Fine-tuning for Code Generation Magicoder: Empowering Code Generation with OSS-Instruct

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.754825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46f18e85-0daf-4bca-878e-b53498adfed9 · outbound

This paper cites WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning.

Data-efficient LLM Fine-tuning for Code Generation WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.759695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e525a6b7-9fa9-4452-be9a-287394605bff · outbound

This paper cites AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data.

Data-efficient LLM Fine-tuning for Code Generation AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.764853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79963254-cf96-4307-885a-60d865c4898a · outbound

This paper cites AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}.

Data-efficient LLM Fine-tuning for Code Generation AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.769581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbb5c7b4-4d46-4234-8fc0-4003b6af5886 · outbound

This paper cites GPT-4 Technical Report.

Data-efficient LLM Fine-tuning for Code Generation GPT-4 Technical Report

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.774463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ca619a6-0435-4600-aed2-70fbd98a3c6c · outbound

This paper cites Training language models to follow instructions with human feedback,.

Data-efficient LLM Fine-tuning for Code Generation Training language models to follow instructions with human feedback,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.778501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5560d7-8c6b-472b-ad48-2e96db58f920 · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Data-efficient LLM Fine-tuning for Code Generation LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.783032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f9b54d-00f3-467a-b473-222cf60939ce · outbound

This paper cites Lima: Less is more for alignment,.

Data-efficient LLM Fine-tuning for Code Generation Lima: Less is more for alignment,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.787971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 470221a9-aa01-4d4a-a272-923a6f32cb79 · outbound

This paper cites From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,.

Data-efficient LLM Fine-tuning for Code Generation From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:52.418218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 29f94bb1-59f3-420c-8417-a34ce4fc310e · outbound

This paper cites CodeGemma: Open Code Models Based on Gemma.

Data-efficient LLM Fine-tuning for Code Generation CodeGemma: Open Code Models Based on Gemma

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.795637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12dc5e46-3cd0-4c9a-b84f-dbabfad62a36 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Data-efficient LLM Fine-tuning for Code Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.800186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d76c548a-5170-45b6-9c82-496cda1e2ab1 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Data-efficient LLM Fine-tuning for Code Generation Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.804821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03acc8e3-97b6-4696-99a4-2af61d640951 · outbound

This paper cites Alpagasus: Training a better alpaca with fewer data,.

Data-efficient LLM Fine-tuning for Code Generation Alpagasus: Training a better alpaca with fewer data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:52.351692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d545b54e-01f4-46e4-ba7d-9aae30e2f139 · outbound

This paper cites #instag: Instruction tagging for analyzing supervised fine-tuning of large language models,.

Data-efficient LLM Fine-tuning for Code Generation #instag: Instruction tagging for analyzing supervised fine-tuning of large language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:52.304022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 91ddc9d3-5472-4969-bcbf-490a5ec08dfe · outbound

This paper cites Rethinking the Instruction Quality: LIFT is What You Need.

Data-efficient LLM Fine-tuning for Code Generation Rethinking the Instruction Quality: LIFT is What You Need

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.818434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c42fbd86-99ef-4c76-8380-0e710a58d3eb · outbound

This paper cites Data Diversity Matters for Robust Instruction Tuning.

Data-efficient LLM Fine-tuning for Code Generation Data Diversity Matters for Robust Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.823269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe55dabc-eab5-4b78-a01b-e186363876ef · outbound

This paper cites InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions.

Data-efficient LLM Fine-tuning for Code Generation InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.828001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4293e59-c409-4f8d-a043-3786c9b60147 · outbound

This paper cites Dataset quantization,.

Data-efficient LLM Fine-tuning for Code Generation Dataset quantization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:52.289850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3aab220-6a4d-4f78-b696-742547f7671b · outbound

This paper cites RECOST: External Knowledge Guided Data-efficient Instruction Tuning.

Data-efficient LLM Fine-tuning for Code Generation RECOST: External Knowledge Guided Data-efficient Instruction Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.836036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b18540f-092d-4e81-87f4-e2e39a77f0f1 · outbound

This paper cites SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning.

Data-efficient LLM Fine-tuning for Code Generation SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.840118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39d91732-39b1-4c99-a979-b90463d5fe69 · outbound

This paper cites Submodular com- binatorial information measures with applications in machine learning,.

Data-efficient LLM Fine-tuning for Code Generation Submodular com- binatorial information measures with applications in machine learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:52.275444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9aab8954-be36-46e6-8744-8a04bfe062a6 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Data-efficient LLM Fine-tuning for Code Generation Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.848251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d04f58b-d45c-45bb-b486-bf46521dbbaa · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Data-efficient LLM Fine-tuning for Code Generation Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.852705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 367e2c8a-0c02-4a9b-a678-aaccd4b2ff50 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Data-efficient LLM Fine-tuning for Code Generation Evaluating Large Language Models Trained on Code

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.856991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75ad3c9a-5776-4431-abab-1667d74e88aa · outbound

This paper cites Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation,.

Data-efficient LLM Fine-tuning for Code Generation Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.860961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2544c58c-47ac-4cda-9f96-02a501e60f1c · outbound

This paper cites Program Synthesis with Large Language Models.

Data-efficient LLM Fine-tuning for Code Generation Program Synthesis with Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.865520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6a5dbb5-0131-4bbf-b7f3-39db63135fa0 · outbound

This paper cites Teaching large language models to self-debug,.

Data-efficient LLM Fine-tuning for Code Generation Teaching large language models to self-debug,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.869856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6819e3c0-827b-42ea-9da9-3fe24297cad6 · outbound

This paper cites Decoupled weight decay regularization,.

Data-efficient LLM Fine-tuning for Code Generation Decoupled weight decay regularization,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:51.874378Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation edcf25bf-0b2b-4cbd-88ff-bf72955a8efc · inbound

Efficient Code LLM Training via Distribution-Consistent and Diversity-Aware Data Selection cites this paper.

Efficient Code LLM Training via Distribution-Consistent and Diversity-Aware Data Selection Data-efficient LLM Fine-tuning for Code Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:36:26.513190Z

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Unavailable: canonical work link unavailable.

Observation 75585882-f0ac-4137-a161-9f10178aa14c · inbound

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code cites this paper.

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code Data-efficient LLM Fine-tuning for Code Generation

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:11.105630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.