Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning | Paper Citation Record

Observation 1d396de4-e817-47f7-b7e9-b7b3b91f1cd5 · outbound

This paper cites Gpt-4 technical report,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Gpt-4 technical report,

Reference 1

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Observation 65edc37e-dacf-487a-8eb9-0a7a5e2cfabe · outbound

This paper cites The Llama 3 Herd of Models.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning The Llama 3 Herd of Models

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This paper cites Qwen2.5 Technical Report.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Qwen2.5 Technical Report

Reference 3

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This paper cites DeepSeek-V3 Technical Report.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning DeepSeek-V3 Technical Report

Reference 4

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This paper cites A Survey of Large Language Models.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A Survey of Large Language Models

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This paper cites Biomistral: A collection of open-source pretrained large language models for medical domains,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Biomistral: A collection of open-source pretrained large language models for medical domains,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:03.616908Z

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This paper cites Finetuned language models are zero-shot learners,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Finetuned language models are zero-shot learners,

Reference 7

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Observation 18a4122d-9ada-4964-9f35-f92d2e582d6a · outbound

This paper cites Lima: Less is more for alignment,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Lima: Less is more for alignment,

Reference 8

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Observation 1e79c7fe-34f9-426a-82d1-216ead68a4bc · outbound

This paper cites From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,

Reference 9

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This paper cites What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning,

Reference 10

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This paper cites Alpagasus: Training a better alpaca with fewer data,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Alpagasus: Training a better alpaca with fewer data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:02.689668Z

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This paper cites Selfcheckgpt: Zero-resource black- box hallucination detection for generative large language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Selfcheckgpt: Zero-resource black- box hallucination detection for generative large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:02.453053Z

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Observation 5dc06f3a-5663-44c2-9668-c87613474b15 · outbound

This paper cites Knowledge conflicts for llms: A survey,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Knowledge conflicts for llms: A survey,

Reference 13

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This paper cites Does fine-tuning llms on new knowledge encourage hallucinations?.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Does fine-tuning llms on new knowledge encourage hallucinations?

Reference 14

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Observation 71ed3bde-e1c3-4d1f-9037-9693f270a490 · outbound

This paper cites ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM

Reference 15

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This paper cites Learning or self-aligning? rethinking instruction fine-tuning,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Learning or self-aligning? rethinking instruction fine-tuning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:01.726250Z

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Observation 85ba1d44-64d2-42ff-8657-1dcb06f79e16 · outbound

This paper cites A survey of knowledge enhanced pre-trained language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A survey of knowledge enhanced pre-trained language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:01.510797Z

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Observation 5eda4091-bfb2-4790-a8af-5787af75c4ce · outbound

This paper cites Large language models on graphs: A comprehensive survey,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Large language models on graphs: A comprehensive survey,

Reference 18

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Observation 80d4977e-ed01-4933-a43c-c3e0624df5df · outbound

This paper cites Towards making the most of chatgpt for machine translation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Towards making the most of chatgpt for machine translation,

Reference 19

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Observation 5cffbdb6-9366-4ffb-b8c1-5681d168da58 · outbound

This paper cites Prompting large language model for machine translation: A case study,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Prompting large language model for machine translation: A case study,

Reference 20

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This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

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This paper cites Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

Reference 22

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This paper cites A survey on aspect-based sentiment analysis: Tasks, methods, and challenges,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A survey on aspect-based sentiment analysis: Tasks, methods, and challenges,

Reference 23

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Observation 56586fd6-958b-4be5-b5c7-139e8b3b30c9 · outbound

This paper cites Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis,

Reference 24

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This paper cites Recommender systems in the era of large language models (llms),.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Recommender systems in the era of large language models (llms),

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:00.827993Z

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This paper cites Collm: Integrating collaborative embeddings into large language models for rec- ommendation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Collm: Integrating collaborative embeddings into large language models for rec- ommendation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:00.623466Z

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Observation 9f5a873d-59a6-4d94-8478-78e58550e5b9 · outbound

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

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning MEDITRON-70B: Scaling Medical Pretraining for Large Language Models

Reference 27

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This paper cites Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:00.439690Z

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This paper cites AlpaCare:Instruction-tuned Large Language Models for Medical Application.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning AlpaCare:Instruction-tuned Large Language Models for Medical Application

Reference 29

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Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge,

Reference 30

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Observation 9aae50ce-95df-4d2e-9950-8c8d242a8459 · outbound

This paper cites Survey of hallucination in natural language generation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Survey of hallucination in natural language generation,

Reference 31

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This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning AlpaGasus: Training A Better Alpaca with Fewer Data

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Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:52.036217Z

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This paper cites Resolving knowledge conflicts in large language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Resolving knowledge conflicts in large language models,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:00.031290Z

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Observation 7a294b64-64df-46cc-b604-9f6b5b5d953e · outbound

This paper cites Knowing what llms do not know: A simple yet effective self- detection method,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Knowing what llms do not know: A simple yet effective self- detection method,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.838185Z

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This paper cites Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.613086Z

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Observation 1824b8df-6ae1-4778-8440-f88a1f06165e · outbound

This paper cites Characterizing mechanisms for factual recall in language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Characterizing mechanisms for factual recall in language models,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.469806Z

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Observation 745e69b8-53e5-41f4-a0a1-a7a6d97279bd · outbound

This paper cites 3ds: Decomposed difficulty data selection’s case study on llm medical domain adaptation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning 3ds: Decomposed difficulty data selection’s case study on llm medical domain adaptation,

Reference 37

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This paper cites Language models are few-shot learners,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Language models are few-shot learners,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.278917Z

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This paper cites Rethinking the role of demonstrations: What makes in-context learning work?.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Rethinking the role of demonstrations: What makes in-context learning work?

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.079788Z

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Observation 9f051c0f-6d08-46fb-9b25-7dcb080c95a2 · outbound

This paper cites 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering

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Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:55.056801Z

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Observation f2e38901-2778-4804-99a5-ff5a5228a427 · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Self-consistency improves chain of thought reasoning in language models,

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Observation d0677557-808e-43f1-85c0-bea677a2471e · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Detecting hallucinations in large language models using semantic entropy,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.819826Z

Source-reported events for the cited work

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Observation d5a59bdc-b0f9-4154-818e-174ffa8b5f7a · outbound

This paper cites Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.542470Z

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Observation be37b4b8-76f4-41e5-954b-7cac5b4becb3 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A Survey on LLM-as-a-Judge

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Observation e5ac1c1b-7ca3-494e-a139-3afcdc72ae8b · outbound

This paper cites M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.311135Z

Source-reported events for the cited work

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Observation 7dc1ae78-6f9b-4a9b-bcc2-7d591f5c5673 · outbound

This paper cites Stanford alpaca: An instruction-following llama model,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Stanford alpaca: An instruction-following llama model,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:53.364402Z

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Observation 6e49bca8-9d8f-4c94-9205-feea6bf9d26b · outbound

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

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:53.442182Z

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Observation 78567fff-a044-4a83-8c21-c352140193fa · outbound

This paper cites Medmcqa: A large- scale multi-subject multi-choice dataset for medical domain question answering,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Medmcqa: A large- scale multi-subject multi-choice dataset for medical domain question answering,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.087203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 812a55b4-f5cc-4043-97bc-025fdd11c0d3 · outbound

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

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning What disease does this patient have? a large-scale open domain question answering dataset from medical exams,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.819254Z

Source-reported events for the cited work

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Observation 51a2eed3-fe37-43ba-8ce3-f345899f4217 · outbound

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

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Pubmedqa: A dataset for biomedical research question answering,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.610019Z

Source-reported events for the cited work

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Observation 5c387b4e-dad7-4de2-bc1d-e14b4cbafcc0 · outbound

This paper cites Measuring massive multitask language understanding,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Measuring massive multitask language understanding,

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Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:53.810143Z

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Observation 6341f18b-71a1-487c-8741-1af0bf8f369a · outbound

This paper cites A benchmark for long-form medical question an- swering,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A benchmark for long-form medical question an- swering,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.440868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 095e1fbb-fb8f-4bba-aa2c-66e7cad9145c · outbound

This paper cites Rouge: A package for automatic evaluation of summaries,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Rouge: A package for automatic evaluation of summaries,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.246418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 21b2d988-8ac7-4cfc-acb1-962b3f9bc556 · outbound

This paper cites Large language models encode clinical knowledge,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Large language models encode clinical knowledge,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.022893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2683c4ee-deb2-478d-9793-31ce0d71355b · outbound

This paper cites Toward expert-level medical question answering with large language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Toward expert-level medical question answering with large language models,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.836579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c7379532-a53f-4516-9fdf-895aefa8aac5 · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Lora: Low-rank adaptation of large language models,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.641966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ba76a535-49af-448f-979b-7ba51080afaa · outbound

This paper cites Towards building multilingual language model for medicine,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Towards building multilingual language model for medicine,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.440110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86989898-bdbe-41b4-aaa9-0de1b9785457 · outbound

This paper cites Med-halt: Medical domain hallucination test for large language models,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Med-halt: Medical domain hallucination test for large language models,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.221661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3cd20c5e-0dc7-40bc-a92e-5a9946dc073d · outbound

This paper cites Xuanyuan 2.0: A large chinese financial chat model with hundreds of billions parameters,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Xuanyuan 2.0: A large chinese financial chat model with hundreds of billions parameters,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.957105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17ec2116-e2b9-44d5-9343-d0afda1769e0 · outbound

This paper cites Debertav3: Improving deberta using electra- style pre-training with gradient-disentangled embedding sharing,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Debertav3: Improving deberta using electra- style pre-training with gradient-disentangled embedding sharing,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.682603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d5b5e99-c269-4fbd-85b1-2f6ef3172e31 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference,.

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning A broad-coverage challenge corpus for sentence understanding through inference,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.483363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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