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Paper Citation Record · LEDGER

Improving Code Generation by Training with Natural Language Feedback

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2303.16749.

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

pith.paper-citation-record.v1
2303.16749 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:58:19.978936Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

10
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6299dcf4-484a-44a0-939d-6c8034328c3e · inbound

Teaching Large Language Models to Self-Debug cites this paper.

Teaching Large Language Models to Self-Debug Improving Code Generation by Training with Natural Language Feedback

Reference 79

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T06:24:24.799760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T06:24:24.607354Z digest=sha256:cc78eef550a06f7dafd4345214cec794622f1babe3badd7d5227479e359bb28e

Observation c7fd8dec-0564-43af-8d3d-a8422fc09754 · inbound

HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs cites this paper.

HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs Improving Code Generation by Training with Natural Language Feedback

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:36:50.191963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T12:36:50.060335Z digest=sha256:96fd4fb81639e3d6d79f2f99912b18efda4701a11bac3fea8cb39a8d5bf0dee6

Observation 35a2ba66-ab2b-4980-a9ce-024fd63519cc · inbound

Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning cites this paper.

Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning Improving Code Generation by Training with Natural Language Feedback

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T10:58:19.978936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:58:19.978936Z digest=sha256:f0109f3f17fa12a0140e02e5473eb28819ab06f1df0196e6a9cd7d7f7c702510

Observation c685b43b-37b2-4958-a33b-ba51977c10ff · inbound

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing cites this paper.

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing Improving Code Generation by Training with Natural Language Feedback

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T15:52:20.360202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:52:20.360202Z digest=sha256:2ddb48b4e0ba7c2a08a23517b90d7d2716d0fcc9bc9e0e368732062f7c6ee52a

Observation 7732469c-31f4-4770-ba0e-a1552b7e2a56 · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Improving Code Generation by Training with Natural Language Feedback

Reference 167

Resolution
unresolved
no resolver link, observed 2026-08-06T14:13:06.545208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:13:06.545208Z digest=sha256:a207969099eb01789b245c1e1b459737ab983aaec2ab73b4c32e62544cec1553

Observation c0cbd4b5-d7da-4869-870a-400d31c3d55f · inbound

Learning from Natural Language Feedback for Personalized Question Answering cites this paper.

Learning from Natural Language Feedback for Personalized Question Answering Improving Code Generation by Training with Natural Language Feedback

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:56:53.022819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T22:55:16.228037Z digest=sha256:51f02a654f57407263290d0bb72e19a50964181bdbca4c522a5890b15b63eb93

Observation c896e01c-6493-43a0-87f8-8d18a22b97b3 · inbound

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation cites this paper.

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation Improving Code Generation by Training with Natural Language Feedback

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T11:39:08.190656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:39:08.190656Z digest=sha256:2e2098afa80b54b274463ffc3a2a0571891595dc036743b24b8f550499ad358f

Observation 73710828-578b-438d-a196-25e6e404d0f4 · inbound

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring cites this paper.

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring Improving Code Generation by Training with Natural Language Feedback

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T19:35:39.085995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T19:33:35.690030Z digest=sha256:d0446d65631f9683e27cfb8609e088a5d076473ba2bdfe41f27ddc5911fb0d56

Observation 8d0ff704-f2d6-40d3-a85d-fc7d912416ac · inbound

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring cites this paper.

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring Improving Code Generation by Training with Natural Language Feedback

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T02:15:52.415460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T01:56:43.707252Z digest=sha256:f57b88c163bb6dfdab788e567d125ae15d5432c6626598d2be341606e805c56b