Paper Citation Record · LEDGER

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 6 inbound Pith citation observations for arXiv:2502.03004.

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

pith.paper-citation-record.v1
2502.03004 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:21:40.369104Z

measured 30 of 30 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:17:06.057653Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T18:57:31.594048Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy5
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d606c452-2c6b-4f53-8162-c4776e73c3ca · outbound

This paper cites an unresolved cited work.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.267218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cd1fb1b-3de2-4c3d-88a3-35bcc9f71866 · outbound

This paper cites B., Agichtein, E., Pinter, Y., and Demner-Fushman, D.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation B., Agichtein, E., Pinter, Y., and Demner-Fushman, D

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:21:40.620203Z

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.

Observation 0f0f41e6-078c-49e7-9198-7cc9df28565f · outbound

This paper cites B., Mrabet, Y., Sharp, M., Goodwin, T., Shooshan, S.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation B., Mrabet, Y., Sharp, M., Goodwin, T., Shooshan, S

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.277725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a4947a1-7acd-4cc7-b017-371d6e773d4c · outbound

This paper cites MEDITRON-70B: Scaling Medical Pretraining for Large Language Models.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation MEDITRON-70B: Scaling Medical Pretraining for Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.283271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 297c7820-f2b8-4c98-b9e1-92c34b6ff225 · outbound

This paper cites OLAPH: Improving Factuality in Biomedical Long-form Question Answering.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation OLAPH: Improving Factuality in Biomedical Long-form Question Answering

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.289485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6642aac-a676-474a-8677-c6cb40ec2ae6 · outbound

This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.294692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab71e79d-8fb8-4ee3-b430-8676ecef249d · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Pubmedqa: A dataset for biomedical research question answering

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.300458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3177628-5aa5-4ae7-9850-4063a8da6cb1 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:21:40.607808Z

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.

Observation 45b428a5-5502-46ea-b0c2-cdc423b55daf · outbound

This paper cites Biogpt: generative pre-trained transformer for biomedical text generation and mining.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Biogpt: generative pre-trained transformer for biomedical text generation and mining

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.308866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f79445d-1398-401e-b35d-db5fad49a82b · outbound

This paper cites Towards Accurate Differential Diagnosis with Large Language Models.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Towards Accurate Differential Diagnosis with Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.313176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 712e9186-6823-49a1-b941-0b162b527d17 · outbound

This paper cites Azure AI Search , 2024 a.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Azure AI Search , 2024 a

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:21:40.595523Z

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.

Observation 8a56ebe3-e243-4a7b-9ea9-63872ff557da · outbound

This paper cites Fine-tune models with Azure AI Foundry , 2024 b.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Fine-tune models with Azure AI Foundry , 2024 b

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:21:40.583978Z

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.

Observation a47f6de7-6dc5-493e-a9c3-46e3340b321a · outbound

This paper cites Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp.\ 227–250.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp.\ 227–250

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.327591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cf4367f-b2b9-49f5-afdd-779a961ff697 · outbound

This paper cites Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.331987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a0f8d89-eaf8-4dc0-ba64-dbd8f95185b3 · outbound

This paper cites Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:21:40.571173Z

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.

Observation fabca648-5370-422c-a9b8-cd79bd2bb226 · outbound

This paper cites GPT-4o System Card.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation GPT-4o System Card

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.340328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb67111b-6017-4f44-8ea6-238f49a35ce8 · outbound

This paper cites GPT-4 Technical Report.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation GPT-4 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.344625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 997cc5ed-a773-41d4-8850-1e3296b95f86 · outbound

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

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Training language models to follow instructions with human feedback

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.348521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cf68e21-c4d8-4de6-9e3a-4abda1c91af3 · outbound

This paper cites Capabilities of Gemini Models in Medicine.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Capabilities of Gemini Models in Medicine

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.352619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82b2518e-d20a-486d-bacc-f656fe57a5b8 · outbound

This paper cites Large Language Models Encode Clinical Knowledge.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Large Language Models Encode Clinical Knowledge

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.356127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 935c1358-2635-4f99-98d1-cbd46dd2f035 · outbound

This paper cites S., Wei, J., Chung, H.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation S., Wei, J., Chung, H

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.359440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2510ac4d-8b00-46ec-8b2a-f6a940c6018d · outbound

This paper cites and Gómez-Rodríguez, C.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation and Gómez-Rodríguez, C

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.362652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9370183-5e75-4add-a8af-2aa91c008a31 · outbound

This paper cites D., Ren, H., Huang, J., Chen, C., Zhou, Y., Fu, S., Liu, W., Liu, T., Li, X., Chen, Y., He, L., Zou, J., Li, Q., Liu, H., and Sun, L.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation D., Ren, H., Huang, J., Chen, C., Zhou, Y., Fu, S., Liu, W., Liu, T., Li, X., Chen, Y., He, L., Zou, J., Li, Q., Liu, H., and Sun, L

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.365846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c12c7e5-6a2c-48ee-af1d-f0f8f5afd185 · outbound

This paper cites write newline.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation write newline

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:40.369104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation bb52f53e-7d87-40fe-87d1-ad33245f5aa5 · inbound

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design cites this paper.

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:30:49.332885Z

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.

Observation ddb8633a-92e1-426d-a5bc-558bc4c58c62 · inbound

CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation cites this paper.

CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:35:52.399458Z

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.

Observation c8214106-188a-41e0-98eb-b0a94d3b5b42 · inbound

AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports cites this paper.

AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T12:32:10.821189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 851b6855-5018-4f0a-9faf-41d2d49c337f · inbound

What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA cites this paper.

What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:33:58.807945Z

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.

Observation a836b743-a870-4f67-818f-5a3a141cdc4c · inbound

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning cites this paper.

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 110

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T18:57:31.595370Z

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.

Observation 8b4428f5-2a7e-4f80-9b25-c3d81ea6ee04 · inbound

When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification cites this paper.

When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T00:17:06.057653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.