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Paper Citation Record · LEDGER

Diagnosing our datasets: How does my language model learn clinical information?

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2505.15024.

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

pith.paper-citation-record.v1
2505.15024 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:29:09.919296Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

53 of 53 outbound references displayed

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  • verified fuzzy20
  • unresolved30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f234cfb-0229-43f0-9ac9-6f1a9d51fcfa · outbound

This paper cites Zero-shot clinical acronym expansion via latent meaning cells.

Diagnosing our datasets: How does my language model learn clinical information? Zero-shot clinical acronym expansion via latent meaning cells

Reference 1

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Observation 99a2bec7-6691-45bd-994a-27d5ecfbbace · outbound

This paper cites Large language models are few-shot clinical information extractors.

Diagnosing our datasets: How does my language model learn clinical information? Large language models are few-shot clinical information extractors

Reference 2

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Observation 7c118357-626c-4b5f-940f-a4a17a0a8817 · outbound

This paper cites Medical large language models are vulnerable to data-poisoning attacks.

Diagnosing our datasets: How does my language model learn clinical information? Medical large language models are vulnerable to data-poisoning attacks

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 458256da-4ca2-4769-98e0-578be6d31f5f · outbound

This paper cites Openbiollms: Advancing open-source large language models for healthcare and life sciences.

Diagnosing our datasets: How does my language model learn clinical information? Openbiollms: Advancing open-source large language models for healthcare and life sciences

Reference 4

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Observation ff145259-2474-4035-8eaa-453546d21e06 · outbound

This paper cites Give me Some Hard Questions: Synthetic Data Generation for Clinical QA.

Diagnosing our datasets: How does my language model learn clinical information? Give me Some Hard Questions: Synthetic Data Generation for Clinical QA

Reference 5

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verified exact
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Source-reported events for the cited work

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Observation fef211be-f041-4639-9482-fe8f1c5f8b8d · outbound

This paper cites Testing and evaluation of health care applications of large language models: a systematic review.

Diagnosing our datasets: How does my language model learn clinical information? Testing and evaluation of health care applications of large language models: a systematic review

Reference 6

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Observation f556a6e8-17ee-4aab-8096-c8549633b3bb · outbound

This paper cites Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias.

Diagnosing our datasets: How does my language model learn clinical information? Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias

Reference 7

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Observation 7a565fea-87e9-431b-9a14-cf70f44e078d · outbound

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

Diagnosing our datasets: How does my language model learn clinical information? MEDITRON-70B: Scaling Medical Pretraining for Large Language Models

Reference 8

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Observation eda3edaf-b7f7-44a1-8d7d-34eec76cafa3 · outbound

This paper cites Med42-v2: A Suite of Clinical LLMs.

Diagnosing our datasets: How does my language model learn clinical information? Med42-v2: A Suite of Clinical LLMs

Reference 9

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Observation 55c9f203-4b69-4d08-b725-6fb1f15a246c · outbound

This paper cites Scaling instruction-finetuned language models.

Diagnosing our datasets: How does my language model learn clinical information? Scaling instruction-finetuned language models

Reference 10

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Observation 528baf99-34de-4f92-85f1-9a62ee7c0396 · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

Diagnosing our datasets: How does my language model learn clinical information? Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 11

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Observation a54525d7-39c3-4dd5-a023-af536ba65c14 · outbound

This paper cites The Llama 3 Herd of Models.

Diagnosing our datasets: How does my language model learn clinical information? The Llama 3 Herd of Models

Reference 12

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Observation 417c29a1-1790-48b0-841d-b33fb7f82d99 · outbound

This paper cites What's In My Big Data?.

Diagnosing our datasets: How does my language model learn clinical information? What's In My Big Data?

Reference 13

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Observation 06020078-e330-4d7f-b11c-206d8adcae3e · outbound

This paper cites Language models are surprisingly fragile to drug names in biomedical benchmarks.

Diagnosing our datasets: How does my language model learn clinical information? Language models are surprisingly fragile to drug names in biomedical benchmarks

Reference 14

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Observation b97d4c36-2a4c-47fc-a8b2-3c0d2fabae84 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Diagnosing our datasets: How does my language model learn clinical information? The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 15

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Observation c403848a-0623-4401-8f01-6895e11b1eb6 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

Diagnosing our datasets: How does my language model learn clinical information? OLMo: Accelerating the Science of Language Models

Reference 16

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Observation 1a74ded9-6b21-4a29-b9a2-cb912f8cbea5 · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Diagnosing our datasets: How does my language model learn clinical information? Studying Large Language Model Generalization with Influence Functions

Reference 17

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Observation e4edb8e6-236e-41c2-933c-94c03b17d46b · outbound

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

Diagnosing our datasets: How does my language model learn clinical information? MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 18

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Observation 30189382-66f0-4b84-ba5f-302319c7f16e · outbound

This paper cites MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain.

Diagnosing our datasets: How does my language model learn clinical information? MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

Reference 19

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8c4f91dd-35ba-4664-946b-e4ecebe47a3c · outbound

This paper cites Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?.

Diagnosing our datasets: How does my language model learn clinical information? Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?

Reference 20

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Observation 57fefea6-580c-43d7-9e02-6859db2b4497 · outbound

This paper cites The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models.

Diagnosing our datasets: How does my language model learn clinical information? The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models

Reference 21

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Observation fb725992-b5eb-4d1e-b1a1-fb34eb9a537c · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

Diagnosing our datasets: How does my language model learn clinical information? What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 22

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Observation 64c726d0-4cdd-46f6-a77f-bb8f2e0ca4d3 · outbound

This paper cites Matching patients to clinical trials with large language models.

Diagnosing our datasets: How does my language model learn clinical information? Matching patients to clinical trials with large language models

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1ead9fe5-3817-411d-be2f-5b0aa66b4eef · outbound

This paper cites Mimic-iii.

Diagnosing our datasets: How does my language model learn clinical information? Mimic-iii

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 15ffc71a-bc0d-4e2b-bee2-69f2ddb0d37c · outbound

This paper cites Mimic-iv.

Diagnosing our datasets: How does my language model learn clinical information? Mimic-iv

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 708b237c-f1dd-484d-8bbb-10c6d554fe39 · outbound

This paper cites Mimic-iii, a freely accessible critical care database.

Diagnosing our datasets: How does my language model learn clinical information? Mimic-iii, a freely accessible critical care database

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T15:29:14.970322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:06.991294Z digest=sha256:ce4198622b360256e89edaad7749660bfa60a60fa10b723c393f920dd01aa149

Observation 447fe995-6908-4da1-98e9-c96bcdb23930 · outbound

This paper cites Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports.

Diagnosing our datasets: How does my language model learn clinical information? Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports

Reference 27

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Observation ae0ea4dc-eb55-4283-abd5-c5356397d8fb · outbound

This paper cites Mimic-iv, a freely accessible electronic health record dataset.

Diagnosing our datasets: How does my language model learn clinical information? Mimic-iv, a freely accessible electronic health record dataset

Reference 28

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Observation 15588577-92b1-486f-9f85-28b44b5d0892 · outbound

This paper cites Large language models struggle to learn long-tail knowledge.

Diagnosing our datasets: How does my language model learn clinical information? Large language models struggle to learn long-tail knowledge

Reference 29

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 148aae8c-38df-421b-9740-7b5f159d9553 · outbound

This paper cites Debunking health fake news with domain specific pre-trained model.

Diagnosing our datasets: How does my language model learn clinical information? Debunking health fake news with domain specific pre-trained model

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation be032bd8-70cb-44a5-85ad-cff94c8f0106 · outbound

This paper cites Can Large Language Models abstract Medical Coded Language?.

Diagnosing our datasets: How does my language model learn clinical information? Can Large Language Models abstract Medical Coded Language?

Reference 31

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Observation 653ef47e-371b-4dd2-9225-be2b3b0049d9 · outbound

This paper cites A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs).

Diagnosing our datasets: How does my language model learn clinical information? A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)

Reference 32

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Observation 33b18d1d-f2a8-472d-8408-a8a9248a7f85 · outbound

This paper cites Are Clinical T5 Models Better for Clinical Text?.

Diagnosing our datasets: How does my language model learn clinical information? Are Clinical T5 Models Better for Clinical Text?

Reference 33

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verified exact
local_arxiv, observed 2026-08-07T15:29:10.339675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:07.815853Z digest=sha256:f9d9912b19882abf88809ae5f64bc29e03c43826452cfdaa043d3a92e40cb905

Observation f4c6fef2-e7be-4481-bff0-f80443ce7a4a · outbound

This paper cites Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens.

Diagnosing our datasets: How does my language model learn clinical information? Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens

Reference 34

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Observation 61f82fdf-f356-469a-8257-6f9df144236c · outbound

This paper cites S2ORC: The Semantic Scholar Open Research Corpus.

Diagnosing our datasets: How does my language model learn clinical information? S2ORC: The Semantic Scholar Open Research Corpus

Reference 35

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Observation ee845d4b-4873-41eb-a54d-0dac2eaf1635 · outbound

This paper cites Fake or real news about covid-19? pretrained transformer model to detect potential misleading news.

Diagnosing our datasets: How does my language model learn clinical information? Fake or real news about covid-19? pretrained transformer model to detect potential misleading news

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:14.211578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 39aebfe4-fe93-4230-9a19-b669f314f29f · outbound

This paper cites Evaluating base and retrieval augmented llms with document or online support for evidence based neurology.

Diagnosing our datasets: How does my language model learn clinical information? Evaluating base and retrieval augmented llms with document or online support for evidence based neurology

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:13.930293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:08.439994Z digest=sha256:18b195986b85ad4f604041e2a84cf69801e94c423f171193105d822afc053fb1

Observation be301167-7843-4da7-b25f-917dcacc47cc · outbound

This paper cites A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

Diagnosing our datasets: How does my language model learn clinical information? A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:13.713212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:08.670802Z digest=sha256:6982eb061f2fd301bebef8707e75274e699edf0dcae2a3aad261e2a8d67ba997

Observation f7be1418-3c17-424e-a2e6-f961bb21df58 · outbound

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

Diagnosing our datasets: How does my language model learn clinical information? Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:08.762683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:08.762683Z digest=sha256:2a5f3bea401e6b321fbd806da330b7ed7439bef5817d8aa34adba69ec5b2cacd

Observation 894cd7eb-1a9f-44c2-8e7b-bc09b43356d2 · outbound

This paper cites It’s time to bench the medical exam benchmark, 2025.

Diagnosing our datasets: How does my language model learn clinical information? It’s time to bench the medical exam benchmark, 2025

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:13.382004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:08.811657Z digest=sha256:7c27a1daab337a78940f337f27b86aa95e5f97c5fa14b550d25e480975e07f67

Observation a347fdee-7fcc-44c3-b722-f201398a60c4 · outbound

This paper cites Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research.

Diagnosing our datasets: How does my language model learn clinical information? Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:08.866790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:08.866790Z digest=sha256:3da20b50489b5b755687f083005f22b2533bdf4de484511a2320d03ba0c6650e

Observation ea42f37f-58f0-4161-8ec9-877c62f6a4c8 · outbound

This paper cites Large language models are poor medical coders—benchmarking of medical code querying.

Diagnosing our datasets: How does my language model learn clinical information? Large language models are poor medical coders—benchmarking of medical code querying

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:13.086715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:08.908349Z digest=sha256:130172402465a8528613279b2132e7d46478cfd74516cfa0d13067510aa0aa33

Observation 193c298b-b779-4c74-b865-d7a5a3faf2d5 · outbound

This paper cites Prevalence of health misinformation on social media: systematic review.

Diagnosing our datasets: How does my language model learn clinical information? Prevalence of health misinformation on social media: systematic review

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:12.805583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:08.958235Z digest=sha256:d8485c0d46842e5e38507bae7455f3a4ec3d27b75fbd169c8ec8a258c8268699

Observation af328636-6b24-405d-9a08-3d00738c805d · outbound

This paper cites Hashimoto.

Diagnosing our datasets: How does my language model learn clinical information? Hashimoto

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:09.010126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:09.010126Z digest=sha256:af183be897b53ad5f515d713f3af2154a0de3cd15fc592c4640377b005023038

Observation 3a2facdc-cb21-47dd-a922-6f946dbfdd1b · outbound

This paper cites RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset , April 2023.

Diagnosing our datasets: How does my language model learn clinical information? RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset , April 2023

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:12.585008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.060653Z digest=sha256:e417a33669f95be8a0bfde7964b68e760181437e37d7cec0b84fc1f3fbf327b7

Observation 89ef4626-a47c-4e1d-9043-a7fe80bfdc97 · outbound

This paper cites Clinical camel: An open-source expert-level medical language model with dialogue-based knowledge encoding.

Diagnosing our datasets: How does my language model learn clinical information? Clinical camel: An open-source expert-level medical language model with dialogue-based knowledge encoding

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:12.321384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.113476Z digest=sha256:cf71ce5cdb146dd0e173eea958366ff9f8ab72ebc9e95e1cccff0a635f2bf080

Observation ba398e54-45e0-46dc-ac6d-267c68b11eaf · outbound

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

Diagnosing our datasets: How does my language model learn clinical information? LLaMA: Open and Efficient Foundation Language Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:09.166078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:09.166078Z digest=sha256:61a88f7b00e2cebff0959bb07efdd053f2e504a9c72312dfc4f5f65124db47a5

Observation 84648d4e-e3ab-4a3a-a53f-5a37f9bda00b · outbound

This paper cites Adapted large language models can outperform medical experts in clinical text summarization.

Diagnosing our datasets: How does my language model learn clinical information? Adapted large language models can outperform medical experts in clinical text summarization

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:12.050114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.232187Z digest=sha256:b175304a75484e2e23fe26f783135a882cb496e29129b8bb15cf60eb15775767

Observation 4824c5f2-d689-44d9-b29b-4d9ac1db0003 · outbound

This paper cites RedPajama: an Open Dataset for Training Large Language Models.

Diagnosing our datasets: How does my language model learn clinical information? RedPajama: an Open Dataset for Training Large Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:09.304268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:09.304268Z digest=sha256:9d8bdd290d225d726a6a229ea4ff124b238cd0c3e5f85bfd7eed640c58b62268

Observation 94fb0127-bc3f-49c5-8a98-afb4e78a4432 · outbound

This paper cites Me-llama: Foundation large language models for medical applications.

Diagnosing our datasets: How does my language model learn clinical information? Me-llama: Foundation large language models for medical applications

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:11.787914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.452087Z digest=sha256:d45017c356a67ac220d8c6ae077836767050c24e6a9faf15a36c1ac1e41a828e

Observation 206aacf5-94cc-47cd-84a1-69a23639e6fb · outbound

This paper cites Almanac—retrieval-augmented language models for clinical medicine.

Diagnosing our datasets: How does my language model learn clinical information? Almanac—retrieval-augmented language models for clinical medicine

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:11.516515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.631319Z digest=sha256:e18c557661e1a8749c0fdffb249191e47b82eddea31faad023474b6e1479aac9

Observation 14102550-5392-445e-a5c8-6da14551a5b1 · outbound

This paper cites A dataset for evaluating clinical research claims in large language models.

Diagnosing our datasets: How does my language model learn clinical information? A dataset for evaluating clinical research claims in large language models

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:29:11.295105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:29:09.803009Z digest=sha256:f57fab3c3f397265c9f16261641252c2e07a93fd89c744cc39f0036b1632a4ed

Observation d3e7985a-3312-49a9-98c1-2a3fbbba001c · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Diagnosing our datasets: How does my language model learn clinical information? Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T15:29:09.919296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:29:09.919296Z digest=sha256:6e5b3bbce62d42a1c43ca2008d039b0b57994b9dcc9f2cfa73898a2a0b4c4678

Pith citing papers

No inbound Pith citation observations are available.