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Paper Citation Record · LEDGER

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go

As of 22 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2511.10868.

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

pith.paper-citation-record.v1
2511.10868 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T22:23:31.201476Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

19 of 19 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b55f708-be09-44bb-9fec-07dcedf6e818 · outbound

This paper cites Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.198026Z digest=sha256:3f543dc2ea928783b95611c25d73fe287ef4e2e2daa8fdadb6178f323ab52ca2

Observation 0e1c081b-adf2-43e3-a8c4-0b3369fa0203 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.369869Z digest=sha256:8c6d17ae9ecfc79d486a5a9b1804cb2d232779ba9932a3583c272db31b5129e4

Observation ea6f2d8d-1743-45a0-94fc-302a58b7536e · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.854689Z digest=sha256:b38a09e12f368c2edbbdef9999850ad3521dc589d250d5bc07d4eb012c4e76a6

Observation 1b2adf9b-2f62-49bf-9a7f-1ba414dd2e9e · outbound

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

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go StarCoder: may the source be with you!

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.950798Z digest=sha256:3b52a9ccf3c45997cff53848bb24dbfe492d448ebb7ca8614546d6286a31929c

Observation 908bab4d-3866-4610-8abb-ab33627ac629 · outbound

This paper cites The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Reference 11

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no resolver link, observed 2026-08-03T22:23:30.099098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.099098Z digest=sha256:307da5b7b1b7b91758d3113a8290e632f50a9df9bbff931f7a9e82e872635cfe

Observation db5029aa-2415-47c3-957e-0d31e9c4b1fa · outbound

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

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go StarCoder 2 and The Stack v2: The Next Generation

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.172268Z digest=sha256:76eb2914353dd296331f86ed062147a82bdf79e6ec446a60a252b14a48abf926

Observation c421f034-5fff-4282-a53f-1bd484c5630b · outbound

This paper cites CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.281906Z digest=sha256:4991d20107086effcdc64df9134e3f4636fe057ddf3c9d65cb90fd0c92a5aa00

Observation cf715578-8170-4fdd-8691-a01ea4334dda · outbound

This paper cites Spaceo.ai.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Spaceo.ai

Reference 14

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no resolver link, observed 2026-08-03T22:23:30.416924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.416924Z digest=sha256:a386ad719f86a3f66717df5e0949c5d634f4c0c0ec39b5bc969c608a31f83337

Observation a2595743-3fe7-4385-be8c-a48cc651f67f · outbound

This paper cites Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B

Reference 15

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T22:23:30.591761Z digest=sha256:305dde29de2be0a9d274a91a01a18015c0b17dfe128f3e814a19a162cc0db34d

Observation efc6b96e-f9fc-44f1-8981-3a9c2a2394cf · outbound

This paper cites CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.701196Z digest=sha256:eb177a3bba48df659a32f87f381fb5b6a402764c96d72c6e2e8170734b5daec6

Observation 64f1acf8-9509-461a-b27c-854699c08ace · outbound

This paper cites Instruction tuning for large language models: A survey.arXiv preprint arXiv:2308.10792,.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Instruction tuning for large language models: A survey.arXiv preprint arXiv:2308.10792,

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.834017Z digest=sha256:6355fd0ef0390a6b5b190407291a2125af375c9539dc8c92850f0c5117c8d118

Observation 1469f486-4021-4d8b-be53-a1ccd05ff185 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:30.982151Z digest=sha256:f170c7e60375ea7059ea307c2f97cf21dadae782412166a8d83fe852a442c8c0

Observation cc68c378-91d1-479d-9eea-b038cf308f2b · outbound

This paper cites linear" seed 42 logging_steps 100 save_steps 1000 save_strategy.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go linear" seed 42 logging_steps 100 save_steps 1000 save_strategy

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:31.135033Z digest=sha256:864597d91eaf38e6070f9b6ac817b02de5e5c602ce827795c9617dd14df719ce

Observation b9775ff5-3da7-4d85-9e66-f7764f7b42ab · outbound

This paper cites none" task_type.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go none" task_type

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:31.201476Z digest=sha256:ae6c9cf172a88e0a46f440d7e2ca0a3f7d8365e7d71d91eceba6062d66caac03

Observation ad6ae605-1634-45e9-87c4-053de515dde6 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

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no resolver link, observed 2026-08-03T22:23:29.749107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.749107Z digest=sha256:b93c3387f3bd0dededfd585d26e6e721ae862cf45b30e139a8dd670dab92f191

Observation ef988659-74f5-4910-be78-244b43053340 · outbound

This paper cites Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models

Reference 2022

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no resolver link, observed 2026-08-03T22:23:28.850280Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:28.850280Z digest=sha256:29394b06790cae29a91430544f430700a54edab0575d6677fb87ea6eab979fd4

Observation 627b8dbe-ed76-4ef9-ae44-c5e4352f74e2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Evaluating Large Language Models Trained on Code

Reference 2023

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:23:29.096871Z digest=sha256:1bbf949fa485a14c103f05e5505e4bad1148552fc168ebedea8818fe71e53cdd

Observation 7144c996-c0d2-4c2e-99b2-26b1f8ddd319 · outbound

This paper cites Measuring the impact of early-2025 ai on experienced open-source developer productivity.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Measuring the impact of early-2025 ai on experienced open-source developer productivity

Reference 2024

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source=pdf_text observed=2026-08-03T22:23:28.972075Z digest=sha256:87fb117417f80538aaebbda57f28679023434903a45ae729b43082515cf0a79a

Observation 6508bb85-1cd9-4206-8b67-05ba870c10c6 · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 2025

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source=pdf_text observed=2026-08-03T22:23:29.667827Z digest=sha256:f0ddd31f02ebd1b8028678ec5425c6c0d8ac94f57a1d3eb55cc223af7908fe7c

Pith citing papers

No inbound Pith citation observations are available.