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Paper Citation Record · LEDGER

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment

As of 11 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.02869.

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

pith.paper-citation-record.v1
2501.02869 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:15.786977Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

37 of 37 outbound references displayed

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  • verified fuzzy4
  • unresolved33
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External citation measurements

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Outbound references

Observation 4330dec0-6b32-4992-a0a6-569db9db295a · outbound

This paper cites URL https://openai.com/blog/chatgpt.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment URL https://openai.com/blog/chatgpt

Reference 1

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:05:15.588983Z digest=sha256:9e671d29d58237e1f0b30b711fa6a870a41fedc1c0a7287fe72233a9d05981c0

Observation f513ce6d-a8ba-4ba0-95cc-e1241d06414f · outbound

This paper cites GPT-4 Technical Report.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment GPT-4 Technical Report

Reference 2

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source=pdf_text observed=2026-08-10T22:05:15.594940Z digest=sha256:db855ca243b21ee95092af279e2bc27f3fe471760c5a70c6f89d82ea5df41df4

Observation 10bbd475-d60b-4b30-9431-50ad9d5dbd46 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment LLaMA: Open and Efficient Foundation Language Models

Reference 3

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Observation 62b6d5c7-9f0d-4e33-8506-c6aee85527ad · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 4

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source=pdf_text observed=2026-08-10T22:05:15.606815Z digest=sha256:d63b827febe2f95586743ccb6b1569df1b5af73119951d56518054bb5f780c21

Observation aec89d59-8cd0-4493-a8f7-770d9c353347 · outbound

This paper cites The Falcon Series of Open Language Models.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment The Falcon Series of Open Language Models

Reference 5

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source=pdf_text observed=2026-08-10T22:05:15.612850Z digest=sha256:765a937821488bbb2200a896cb86c3ff77c2ad20f65291afb0f23789c5354223

Observation 8f5258a6-c76d-4872-aa93-a0b752967ba1 · outbound

This paper cites Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca

Reference 6

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source=pdf_text observed=2026-08-10T22:05:15.618917Z digest=sha256:cdf7d33f0bb0313ef1ddd6b75afd1d0835163a79d59f3d5a7f031cb52adb7297

Observation 37e98863-2295-43a0-a28b-eb742496bffe · outbound

This paper cites Qwen Technical Report.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Qwen Technical Report

Reference 7

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source=pdf_text observed=2026-08-10T22:05:15.626746Z digest=sha256:bc587e26eeafd506253a460208b598528148bc7e2804c40b356fa8644d2b5331

Observation 82f2840d-b0ee-4bbf-af22-37010e8949a7 · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Baichuan 2: Open Large-scale Language Models

Reference 8

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source=pdf_text observed=2026-08-10T22:05:15.633011Z digest=sha256:65ac0be916722391252fc693cdd30c138a539f12d2bd2179c687d5e564148c1c

Observation 8b89e696-75fd-49d6-91a1-ae2bcca3a28f · outbound

This paper cites A Survey of Large Language Models.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment A Survey of Large Language Models

Reference 9

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source=pdf_text observed=2026-08-10T22:05:15.638885Z digest=sha256:b45e46e7de9b22dca54de2d757f645c8e93f57b61dfa48a9b199b6f9da2dfa3a

Observation a66cdc01-4768-4f9c-957c-f8973488ff3f · outbound

This paper cites Singhal, S.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Singhal, S

Reference 10

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source=pdf_text observed=2026-08-10T22:05:15.646379Z digest=sha256:c20e1d560c0dc64b7c5d676071950e0f19bf03f4a205aa6c1210aa4c7a7b6cd0

Observation 2cee493c-f207-4add-8c8d-c2e158627ead · outbound

This paper cites DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task

Reference 11

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source=pdf_text observed=2026-08-10T22:05:15.651711Z digest=sha256:e38d475323a87e531be9dfb8cc5a87814bed6a1d2f4fd5759e18d576abb4487b

Observation 91c9a53f-e670-4195-b077-3e077abd4a4d · outbound

This paper cites HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

Reference 12

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source=pdf_text observed=2026-08-10T22:05:15.656586Z digest=sha256:c5834f6ba40a8c872c659a7980712efb33943e595841b9f5d573f4b3ce0f3215

Observation d41c88eb-b2c3-4bbc-ba39-3b352fd9e937 · outbound

This paper cites HuatuoGPT, towards Taming Language Model to Be a Doctor.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment HuatuoGPT, towards Taming Language Model to Be a Doctor

Reference 13

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source=pdf_text observed=2026-08-10T22:05:15.661886Z digest=sha256:653e2db16975932869de0f4642ce78c39aecc219d8bd9baaf93fe00d7a363b4c

Observation ca7ab5eb-d335-4576-aa34-ae98dc49322c · outbound

This paper cites Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue

Reference 14

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source=pdf_text observed=2026-08-10T22:05:15.667202Z digest=sha256:0fda5c4cb24ee42932af38bcf2ecaf4b084b39fffc6283ebfb356174451ec5fe

Observation f26aeb8d-c8c7-4ea2-857a-7ca2a5a46bda · outbound

This paper cites Pre-Trained Models: Past, Present and Future.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Pre-Trained Models: Past, Present and Future

Reference 15

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source=pdf_text observed=2026-08-10T22:05:15.672640Z digest=sha256:d2c4e0d9f1481f3f20794f8efc4d8d31366ac2cc54fe213265cefe17e0139b56

Observation 2a0f0594-d25b-434b-bb1a-8751be44968c · outbound

This paper cites LIMA: Less Is More for Alignment.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment LIMA: Less Is More for Alignment

Reference 16

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Observation b5bf77cf-c4f6-49fd-8fe1-83653676386d · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-10T22:05:15.683833Z digest=sha256:0db344dd645c79e1cfb58ba15b3a2179deb81198e5d27d4ae355786af122e9a8

Observation 64f9c5d7-1d19-4ce2-ad75-234e9349bbf3 · outbound

This paper cites Howard, S.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Howard, S

Reference 18

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Observation f3faf965-a070-4ebd-9f8d-2dc33691a8e0 · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-10T22:05:15.696005Z digest=sha256:0cece89e789549a1776268bd2e27f5bfc9a9c58d52d8aed18857373b18b9b43e

Observation 7b4d32de-8dfc-451a-9597-8b202ba6d2e7 · outbound

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

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 20

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source=pdf_text observed=2026-08-10T22:05:15.701144Z digest=sha256:f630f269bcac6154da21c3cadbbbd22995757995df2116687c619d1d243db490

Observation e103d313-3241-483b-a258-2dd98de48ed8 · outbound

This paper cites Policy Optimization in RLHF: The Impact of Out-of-preference Data.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 21

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Observation 5bd1c7ef-d7cf-41e4-b49a-5474a408c627 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 22

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source=pdf_text observed=2026-08-10T22:05:15.711361Z digest=sha256:320e81b40be63bb5311dd73b71bbea8b365e30770ef4b2915b15913fa226151e

Observation d1d7d6be-e960-46e2-a204-14554a4abb8c · outbound

This paper cites Proximal Policy Optimization Algorithms.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Proximal Policy Optimization Algorithms

Reference 23

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source=pdf_text observed=2026-08-10T22:05:15.716694Z digest=sha256:74c42d8a855cdd8bdc431be62eee17bf1f1709f03ea455e5cf75e21f8dcf3de5

Observation 749ee04f-2c0c-445e-bb02-2ed508acb4ad · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 24

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source=pdf_text observed=2026-08-10T22:05:15.721039Z digest=sha256:f496a99887a8a9055c3e24232e33db36feafae99783b8df97ecec7774fcea5d6

Observation 33c2bc84-eab7-4701-bb11-9e5916ba6190 · outbound

This paper cites Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence

Reference 25

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source=pdf_text observed=2026-08-10T22:05:15.726019Z digest=sha256:f988601cbd82cf664662eb309376383bc52db01f42439882693e0815b58c7140

Observation 156136cb-564c-486c-8640-d818d98ac631 · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-10T22:05:15.730714Z digest=sha256:a234c7e7bc3543564d5c4aa22571d62d8271fffa68bebfad1d4adfd6fdec9f83

Observation c8b1618a-ac7a-41f4-b005-50f8ab124e20 · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 27

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Observation fa6c3b33-9a3c-4aff-8daa-3bfd76808fcf · outbound

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

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 28

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source=pdf_text observed=2026-08-10T22:05:15.740630Z digest=sha256:29b5e49860240541dff029e4c2a99e9bbb3addbe25258a075dbbc05684ee09b0

Observation 48604f80-b04d-489e-809c-532d6ddffe4b · outbound

This paper cites ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

Reference 29

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Observation 2816e9f4-662d-412f-b550-075203237019 · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 30

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Observation f91cbb31-ce5e-47d3-b9d5-39e3a1be8f4f · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 31

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Observation e58be108-c895-4cb5-88a1-e0454f62567d · outbound

This paper cites Aghajanyan, A.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Aghajanyan, A

Reference 32

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Observation 41deca27-fed1-4755-a513-2bf602de1e73 · outbound

This paper cites an unresolved cited work.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Unresolved cited work

Reference 33

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source=pdf_text observed=2026-08-10T22:05:15.766061Z digest=sha256:50c10bba6ca14f6d090bd7676e6ae576b431ddb134b56a86061275eeb4990756

Observation 0e7280e9-f8c4-4aa5-876c-a3c4ad4434bc · outbound

This paper cites Loshchilov, F.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Loshchilov, F

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:05:15.771774Z digest=sha256:3d062fe53ea784b2068d529b584c9f8ef83a487b7422ff6b73671199d2646e13

Observation b6070f1f-c61a-40a8-84e3-4a581087e07d · outbound

This paper cites Huatuo-26M, a Large-scale Chinese Medical QA Dataset.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Huatuo-26M, a Large-scale Chinese Medical QA Dataset

Reference 35

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source=pdf_text observed=2026-08-10T22:05:15.776787Z digest=sha256:78bed32d827d54a0d854aa256af1e0cb9db0689f8d0062f0b60d97f78b214135

Observation 0b3799aa-78a4-450d-84be-64ffd12e4dcc · outbound

This paper cites - Clearly and concisely explain complex medical knowledge.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment - Clearly and concisely explain complex medical knowledge

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-10T22:05:16.318856Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:05:15.782109Z digest=sha256:2bcf70425cc20d1345ba702aae4b0624bdb1bf0ade3acfe68bd4c43f3c776782

Observation 4d60b46c-4151-4eda-9bfc-9a88e67765ec · outbound

This paper cites Answer1” relative to “Answer2.

IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment Answer1” relative to “Answer2

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:16.300084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:05:15.786977Z digest=sha256:cf46832d759f899d9c4a95bd9557d5c18f25766663eacdb8e9d0e9ffb31f15ec

Pith citing papers

No inbound Pith citation observations are available.