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Paper Citation Record · LEDGER

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection

As of 23 August 2026, this Paper Citation Record lists 90 of 90 outbound references and 0 inbound Pith citation observations for arXiv:2505.05327.

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

pith.paper-citation-record.v1
2505.05327 v2

Coverage vector

measured 90 of 90 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:12:39.145675Z

measured 90 of 90 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

90 of 90 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved87
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd9d5d69-8c74-48a0-b71f-16f25603f3cc · outbound

This paper cites GPT-4 Technical Report.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection GPT-4 Technical Report

Reference 1

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source=arxiv_source observed=2026-08-15T23:12:38.727334Z digest=sha256:fb024f10e32dfaa7a88a06fc65ac92205ca2f1deaeb17f71133f6b98bab6a459

Observation f5c326ee-28f4-4f9d-ba52-79046a613e15 · outbound

This paper cites A Survey on Data Selection for Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection A Survey on Data Selection for Language Models

Reference 2

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source=arxiv_source observed=2026-08-15T23:12:38.732189Z digest=sha256:ec9fab916026c595cba3e882c5fed63f1578fd727ec40e937482c85d2fcd1068

Observation bf2938b2-58f4-492c-9dda-6558c184aaa5 · outbound

This paper cites Data Diversity Matters for Robust Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Data Diversity Matters for Robust Instruction Tuning

Reference 3

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source=arxiv_source observed=2026-08-15T23:12:38.737164Z digest=sha256:5c84381ea6c2ab9564d04d5d5bc158e5d700a034751d459e9c4a974855728da5

Observation 155a8d2c-3da2-4604-87a0-b801db279be9 · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 4

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source=arxiv_source observed=2026-08-15T23:12:38.741716Z digest=sha256:22df328f32c419002cf1cb0beb82669dd732a8555a9094a027d0db718b9a937e

Observation 69310fdf-ae98-449e-8d42-1d3e7a7591f0 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-15T23:12:38.746103Z digest=sha256:5828f47c0ed3f89a11d2290b4196b4240670c2d8ba1aaf81f123a61f821c0312

Observation e05806ae-f0cc-43e5-a94d-f06e0f96300c · outbound

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

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 6

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source=arxiv_source observed=2026-08-15T23:12:38.750650Z digest=sha256:5e39eefec2ab3a39fffb771e48d5e27da95626bc57e9d3b6466ea39a7715593d

Observation 1505fe1d-d738-4dc1-9e5d-3a053a8c321f · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-15T23:12:38.756503Z digest=sha256:062de5a296ca5d90d4f3fa62815ae7719cf53d7bfc3dbf6e87c788983ee3470e

Observation 26e73836-cc37-4fdc-a1c5-2ee5cef27863 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-15T23:12:38.760347Z digest=sha256:ace4163c83be08166548bd36c940407d726dc372dd9ec9af3998472029be9dd9

Observation 79d21928-ce20-48eb-a137-2071da52daf0 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

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source=arxiv_source observed=2026-08-15T23:12:38.764513Z digest=sha256:651f5a89221143bcd453ea728ffd2ffc3e4bf5bcc18d447470949ad14a27d0fa

Observation 374dd220-9b3b-46fd-b447-83854c0713e2 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-15T23:12:38.768838Z digest=sha256:8774e4394b59ebcc21403cc9f616909ddb5c69303e7d08d56d232955af5f338e

Observation 55fb70a7-6dbc-4b60-bf6f-232c7f563fdf · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-15T23:12:38.773535Z digest=sha256:4c665a7e2f1f01c8c8c534bff96b2affa012ec9d205fd61e1ac43dacd2e626b3

Observation fd3ffeb1-4413-484c-be72-368839df98a6 · outbound

This paper cites Large Language Model for Science: A Study on P vs. NP.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Large Language Model for Science: A Study on P vs. NP

Reference 12

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source=arxiv_source observed=2026-08-15T23:12:38.778214Z digest=sha256:249d5f5e028f744485eb8c7a2ae904d26de84edacda0162ed25e39e1c84d6512

Observation 4d0f7236-36c3-4fc8-8530-80d3ff3ef2f2 · outbound

This paper cites A Survey on In-context Learning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection A Survey on In-context Learning

Reference 13

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source=arxiv_source observed=2026-08-15T23:12:38.783773Z digest=sha256:607225ed543e8d45c3b02b7e770e047548bcfcb4c5f36cfa9814a52e90bb8e89

Observation 63f11647-b396-495f-b43d-811fea273590 · outbound

This paper cites MoDS: Model-oriented Data Selection for Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection MoDS: Model-oriented Data Selection for Instruction Tuning

Reference 14

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source=arxiv_source observed=2026-08-15T23:12:38.788551Z digest=sha256:887848c9e7335358e16f92192eb397d37d4695baada99a4643b08356a2bcca55

Observation faa5277a-c06f-4335-aee7-adee7f33d009 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-15T23:12:38.793494Z digest=sha256:bd01d0f412dad69045320f8b37e0bafd2b60582d6a5e1cb4a863f51cd95b97e6

Observation a73ff247-19cc-4a0a-a228-8391d26e3a75 · outbound

This paper cites Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation

Reference 16

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source=arxiv_source observed=2026-08-15T23:12:38.798113Z digest=sha256:52c3fdb06b9b354e36848767991a56a25125fabbb26c8243e34037ba1871d3c7

Observation 89a8ce83-3be8-45e4-8adf-3fd96bcc8c28 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-15T23:12:38.803340Z digest=sha256:88991201b5e61c6a1f2d7545faa7c2c493ce8162e01e2b528ecd9edea78e6732

Observation 9c0a76c3-2098-4e4f-a4ce-88d07762ab38 · outbound

This paper cites The Llama 3 Herd of Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection The Llama 3 Herd of Models

Reference 18

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source=arxiv_source observed=2026-08-15T23:12:38.807962Z digest=sha256:031eb072e2893ab0d529d86ac90382d98c4df05029d56dc0c9f4ea3bda889f57

Observation 66b9d336-d6a9-40b2-b5c3-9e2214d2b7d9 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 19

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source=arxiv_source observed=2026-08-15T23:12:38.812433Z digest=sha256:a66ea4216c116e1b3320b3225586d76fb491ff84eb529a766965ae1c591b0a23

Observation 638ac45a-4c4c-48b8-84e0-9a1823d212e8 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-15T23:12:38.817778Z digest=sha256:e05a9230f1b56c51bb49c10097abc902247fd8523e0427b26a96477c3eca9707

Observation 1f160d64-1e72-4064-b628-117ebb2a9a36 · outbound

This paper cites SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

Reference 21

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source=arxiv_source observed=2026-08-15T23:12:38.822550Z digest=sha256:518426c4a368709cd98df475013219c76f03fc2b8006bc8bcb784e2b28ebc0e2

Observation 8aae2c85-a410-4095-a8b6-e9b29f5c08ef · outbound

This paper cites Measuring Massive Multitask Language Understanding.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Measuring Massive Multitask Language Understanding

Reference 22

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source=arxiv_source observed=2026-08-15T23:12:38.827021Z digest=sha256:f4181ee3ae06702ff29c7660b83f9cb1f17aadd7a55f8b521e0218a0359c2814

Observation 274a9e3d-8836-4c93-97df-00b4aafc877b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection LoRA: Low-Rank Adaptation of Large Language Models

Reference 23

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source=arxiv_source observed=2026-08-15T23:12:38.831572Z digest=sha256:9d86f6a9cf4479e11818d92075c44144140cedbcfe7012793f4bc875343da089

Observation 8b19eb5a-b1ed-4b4e-b86d-51b4443c117c · outbound

This paper cites GPT-4o System Card.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection GPT-4o System Card

Reference 24

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source=arxiv_source observed=2026-08-15T23:12:38.836119Z digest=sha256:ee3932c4d2195796f99f1226acc7fed90811bd5bdf7d8e135fd4f973016a3f0b

Observation e36c28f5-9124-47e8-9600-cfeb7aa3413d · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-15T23:12:38.840508Z digest=sha256:fd3e2951840cd346a9651445c90b916387fe8e38e8c6e959bd3cfbf20c457236

Observation 77a240b9-b37b-4ab1-a5cc-c808ed2ee27f · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-15T23:12:38.844640Z digest=sha256:c3afdf22506c14a635403fa19a38676475f475de7ffdd8642b88c17e074244aa

Observation 984c3634-7ef7-4c1f-8bb9-90b624d07bee · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-08-15T23:12:38.848770Z digest=sha256:bf4aaf1f50fc113daeacceb7bd2decf698caca91975ea47e1ed4197cdbdf4e51

Observation 2f9e863a-9499-4a66-a9f3-309762a3e73d · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.852876Z digest=sha256:f9bd38df2d9401c36516d15dcd9e14b886b6e8338992a9c1ea486b45f53e6b4b

Observation 4eb4f3ea-2ccc-4d3c-90d4-e8e65a614ad6 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Adam: A Method for Stochastic Optimization

Reference 29

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source=arxiv_source observed=2026-08-15T23:12:38.857219Z digest=sha256:cb1e2392d8c211a8679acb582be07a0cdfc54c1a143d9e5bd3fac1606ce14ec7

Observation a45fe65a-971b-40a6-88f8-6c68c470df42 · outbound

This paper cites o pf, Yannic Kilcher, Dimitri Von R \.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection o pf, Yannic Kilcher, Dimitri Von R \

Reference 30

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source=arxiv_source observed=2026-08-15T23:12:38.861510Z digest=sha256:0a9c0b31a4daeff179b65358aa6da2ccf6c4d14896dbabb889f8809943379938

Observation 9585d641-07c0-4d6d-a5af-36926916e6a5 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 31

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raw_fallback, observed 2026-08-15T23:12:40.276999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.866457Z digest=sha256:d19e36c3fb2569410dcdffb48ce55a8150befb0b1ff79a47bcb8df2269b25450

Observation 945aeba8-af43-43cd-9a35-8f1927ad9e6d · outbound

This paper cites Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning

Reference 32

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source=arxiv_source observed=2026-08-15T23:12:38.870415Z digest=sha256:5fe5b76e51892014049d94bf3cf432defddeaeca95955904c21f5b0e6801eb38

Observation f97c4c13-5626-4237-81fb-db9720d797f0 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 33

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.875070Z digest=sha256:36cdf22dc555128ac81d79b103b22112bc09b9b54b892dd16797f299840248b9

Observation 1932b0a6-d289-4e7f-8b05-3e771f63f7ca · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 34

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source=arxiv_source observed=2026-08-15T23:12:38.878973Z digest=sha256:894802493d5de5168562e8075993d2ae5a0d814e893eda2878128e58cf7f3dea

Observation 3738e0cb-a10b-4fa2-afcf-cb98ade8e090 · outbound

This paper cites Hashimoto.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Hashimoto

Reference 35

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source=arxiv_source observed=2026-08-15T23:12:38.883497Z digest=sha256:b6d740a5b3036d7fdb021e2c81286e4bcb8ff5aac3b5e5df2a9fd6c63a569d97

Observation fb0d58bb-1895-47e8-ae72-8b1c2dab14d4 · outbound

This paper cites One-Shot Learning as Instruction Data Prospector for Large Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection One-Shot Learning as Instruction Data Prospector for Large Language Models

Reference 36

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source=arxiv_source observed=2026-08-15T23:12:38.887658Z digest=sha256:517b64e57b5a41d7b705e0b0b52e5b4b9e582a116319e43a82e92a8b2a00b7a2

Observation bac71c52-6c56-454d-812c-918a6e997319 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.891818Z digest=sha256:d28cb12e25decf2a790c3d4566672dcd7e4bc15e7ff3903b577faf464b7a8135

Observation a90277f8-257d-47b9-b9ec-6aa1894849b0 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 38

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:38.895763Z digest=sha256:4732db8e4e601115644a5ddfef354d7abb22126e589164a213d96f466f4ce3c9

Observation 61ccaff4-400f-488d-96e6-8d4cdc00d5e9 · outbound

This paper cites MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

Reference 40

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source=arxiv_source observed=2026-08-15T23:12:38.906626Z digest=sha256:db056e301148708fe1476e704f4fe87378e098dc0f933a293718ed7c26bd3311

Observation 92df57d4-0a25-4a8e-8b4a-2fbb1bfbf471 · outbound

This paper cites LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 41

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source=arxiv_source observed=2026-08-15T23:12:38.911124Z digest=sha256:0653d42172fc364ba72fc83db54d1b866d4b4252a7abb92cf40eeef82a2c2dad

Observation dc205ad5-cc87-4cb6-9226-8aebb10f1427 · outbound

This paper cites Wong, Dongfang Li, Ziyi Wang, Baotian Hu, and Min Zhang.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Wong, Dongfang Li, Ziyi Wang, Baotian Hu, and Min Zhang

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-15T23:12:40.221064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.917261Z digest=sha256:4c8dd65c39064ec35350652659ab79fe849100bd093e4773d764334971a45102

Observation 1ba970f9-edf8-4966-a405-6095fb29b19a · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 43

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source=arxiv_source observed=2026-08-15T23:12:38.921986Z digest=sha256:205fccab64f43d54389b3348c0cd4ffd110209917555b2c4b3ef84a4136880e1

Observation 8508daa3-73fa-45b1-942c-0a51483f64c4 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-15T23:12:38.926884Z digest=sha256:4766fe0dd296803f6be5fdf3c4a73b012c742b8a019ca31a359bd34ff26939ee

Observation 2a11c1d2-d05a-418e-9d11-0160d0997a35 · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 45

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no resolver link, observed 2026-08-15T23:12:38.932003Z

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source=arxiv_source observed=2026-08-15T23:12:38.932003Z digest=sha256:81dc57d05bf71ead3c962393b61653030622d16353efe4c843ff090f13cce9a9

Observation a8d66043-f8b3-41c1-81e8-109c35da128d · outbound

This paper cites In-context Learning with Retrieved Demonstrations for Language Models: A Survey.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection In-context Learning with Retrieved Demonstrations for Language Models: A Survey

Reference 46

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no resolver link, observed 2026-08-15T23:12:38.937476Z

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source=arxiv_source observed=2026-08-15T23:12:38.937476Z digest=sha256:4909e87c0809e705426c3761c14e7c35cc491e57938f51a3b8867aa402eab0f8

Observation bcdbe146-8f7c-4a6d-bc9f-3377f33cdb8e · outbound

This paper cites Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates

Reference 47

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no resolver link, observed 2026-08-15T23:12:38.942328Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:38.942328Z digest=sha256:994d4f23907a7984e05b8f32892af5cd89695837b24b46d3b6eb9a4e2bbf912e

Observation 4c1b6d9e-d006-4037-843a-ba6df53ed5ed · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 48

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unresolved
raw_fallback, observed 2026-08-15T23:12:40.195273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.948020Z digest=sha256:b11d061479c240b0d8529e032b39f2363432f7d1208e730b14e9d7ed9a8f1ef5

Observation 7e5070fb-bee9-44b2-99d4-bff9af8c6a58 · outbound

This paper cites The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph

Reference 49

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no resolver link, observed 2026-08-15T23:12:38.952392Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:38.952392Z digest=sha256:5d07b20412878f0be4dc72001374a8c05c8d429ca8df86404deeccc2350e91a1

Observation 29c27e66-ddee-4339-b4c7-43e05d1b79fe · outbound

This paper cites Cross-Task Generalization via Natural Language Crowdsourcing Instructions.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Reference 50

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no resolver link, observed 2026-08-15T23:12:38.956544Z

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source=arxiv_source observed=2026-08-15T23:12:38.956544Z digest=sha256:4a849822ececad433a7cdc5fc97a08624abac8df1dd66b44ef36f9c96230c832

Observation 85339d18-b535-44c2-9b95-c0fccfe15ab8 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 51

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no resolver link, observed 2026-08-15T23:12:38.961438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:38.961438Z digest=sha256:b3dc297dd8781833d7fd2a3015e6a96e46a663278421f0ea44e55dd0ebcfb29f

Observation 309ca5f2-7ad2-4736-a26b-949df69951a1 · outbound

This paper cites Chatterji, Faisal Ladhak, and Tatsunori Hashimoto.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Chatterji, Faisal Ladhak, and Tatsunori Hashimoto

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:12:40.169541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:38.965876Z digest=sha256:799b0e7155ec49b07b413aa35ba19a3184941be19c701fe9370406efce134b0c

Observation 41958aec-42c1-4d19-abc4-64c44ef6fdf9 · outbound

This paper cites West-of-N: Synthetic Preferences for Self-Improving Reward Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection West-of-N: Synthetic Preferences for Self-Improving Reward Models

Reference 53

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no resolver link, observed 2026-08-15T23:12:38.970109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:38.970109Z digest=sha256:8a2b917803e05384738bc1230b651df363608d5796d9d41e2370f40362bb4efa

Observation 09014e26-d1ca-431d-9aa2-429fd75c3604 · outbound

This paper cites G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation

Reference 54

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no resolver link, observed 2026-08-15T23:12:38.974313Z

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source=arxiv_source observed=2026-08-15T23:12:38.974313Z digest=sha256:a9562d8c4dd6508a2c9e4fa2d0c31aa4242119ae73b738f5670f094a8677687c

Observation 234c6235-7e6d-48e3-9e4c-42dae165a7dc · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-15T23:12:38.979065Z digest=sha256:d63d4f1b936d79da0c92585140dbdb8a8b7be9e5567d4dd3ef71058c83f6d2a3

Observation d2868183-5e91-48a5-bc8c-4b45624d6d2c · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 56

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source=arxiv_source observed=2026-08-15T23:12:38.983039Z digest=sha256:d818954bc6118f44c98111ad08a932e5912e2b86e8c5118647f8fb995233ec75

Observation 10bb5924-b215-404e-9bd8-172c4e3940ba · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-08-15T23:12:38.986958Z digest=sha256:96d30ac7a60aa61a1bcceb68067feab11dd6ac5f492fd6590cf3d7be8690bcf6

Observation 65e1b963-2a35-4138-be8c-9f5d855ce395 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 58

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no resolver link, observed 2026-08-15T23:12:38.990830Z

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source=arxiv_source observed=2026-08-15T23:12:38.990830Z digest=sha256:926c8ad8145cc6bfcb51c2144065e9748cfd3d12de9a16746af6bb786169b1cb

Observation 8f13eb24-f868-4b2d-a2b1-be25ed6693ad · outbound

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

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Reference 59

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no resolver link, observed 2026-08-15T23:12:38.994562Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:38.994562Z digest=sha256:43bf233a4a4980e62dd85dbfb5e22e08e4ed4a9aceb0ae7e4ce989383fabc9ae

Observation 7b6c26ed-c65f-422d-b62f-b581436d8eba · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 60

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source=arxiv_source observed=2026-08-15T23:12:38.999782Z digest=sha256:5b23ee95891570e9a6736fa41a24acc842a889faadf74bc2d312b57e0030495e

Observation 278dc396-bfd7-4e73-8cfd-3a41c777d48d · outbound

This paper cites MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Reference 61

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no resolver link, observed 2026-08-15T23:12:39.004174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.004174Z digest=sha256:d8495fff9ddd4e2847c4d50d8343ba23b0f20526d5c9ffdc5d7d4cd53e52ab72

Observation 2424447c-aeea-45bf-90dc-6fd0e09183ed · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 62

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no resolver link, observed 2026-08-15T23:12:39.009061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.009061Z digest=sha256:43ea0ee5a260873f6c55da55974f3a016f237f4388194f8bf2d338a8a4905960

Observation b3a1c8d5-e5fe-4de0-95ef-a04cd2b9d02d · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 63

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no resolver link, observed 2026-08-15T23:12:39.014647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.014647Z digest=sha256:956cda7e58907eb5404bc0800b91248d56d26e2062f6b9e00dc17b589211c46c

Observation 79b60c36-1cb4-4928-beaf-f72765f9cba8 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 64

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unresolved
raw_fallback, observed 2026-08-15T23:12:40.098295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:39.019197Z digest=sha256:7c1a5b267b015beb37f2ecbf49b3c7283c9951b542b2bda533e906778c92af84

Observation 6677704a-7136-460d-90dd-84d044c05628 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Gemma: Open Models Based on Gemini Research and Technology

Reference 65

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no resolver link, observed 2026-08-15T23:12:39.024224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.024224Z digest=sha256:1c69db013f1993bb2bd863b875cbd23df7b4d07c330e58bc767cb2450c74ea33

Observation 6c6f281a-8f62-4b83-a282-f28b9ef04b28 · outbound

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

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 66

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no resolver link, observed 2026-08-15T23:12:39.029474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.029474Z digest=sha256:d7edec7d2ec839dfff97cabf24cbe5126670fe80f1b93889efa6529d30b8bf0a

Observation 8e41bb2d-f502-4929-ab3f-e80139e22935 · outbound

This paper cites Koala: An Index for Quantifying Overlaps with Pre-training Corpora.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Koala: An Index for Quantifying Overlaps with Pre-training Corpora

Reference 67

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no resolver link, observed 2026-08-15T23:12:39.034183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.034183Z digest=sha256:c979e9efd878ff81879d63e496cafb78e97886491bd9280e805e707f27168a20

Observation ee908539-ab64-4e1e-bf8a-2a73813c27e6 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 68

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no resolver link, observed 2026-08-15T23:12:39.039117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.039117Z digest=sha256:4f2347204ed4bd810c63692413ba174263f7fb74219039c2eac1f96677df9230

Observation f0d2ee6b-a41e-4089-9847-b0639ec1da16 · outbound

This paper cites Large Language Models are not Fair Evaluators.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Large Language Models are not Fair Evaluators

Reference 69

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no resolver link, observed 2026-08-15T23:12:39.044133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.044133Z digest=sha256:615a1f463aebc3864b8933b28462cd04e0559498e963f9d2f6c3690e9ffae733

Observation 1ba51fdc-7ed2-43e9-972a-7ce21f880ebd · outbound

This paper cites Shepherd: A Critic for Language Model Generation.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Shepherd: A Critic for Language Model Generation

Reference 70

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no resolver link, observed 2026-08-15T23:12:39.049034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.049034Z digest=sha256:602824711d1ff611edc7c24b8f6ad0df953b3c1f85285f9c0addf1d487a56ccc

Observation 782bc5f4-c9ce-4dd4-b48b-ba858c61db29 · outbound

This paper cites How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data

Reference 71

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no resolver link, observed 2026-08-15T23:12:39.054426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.054426Z digest=sha256:9a3d4c32c1a98a0992edd59137634bfa1b4726eb780475c62e465dd4e3238d49

Observation a91e4b30-8ed0-4ca0-a8ce-7c5bee44664f · outbound

This paper cites Perplexity from PLM Is Unreliable for Evaluating Text Quality.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Perplexity from PLM Is Unreliable for Evaluating Text Quality

Reference 72

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no resolver link, observed 2026-08-15T23:12:39.058902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.058902Z digest=sha256:ecc18b6197d5511be262e9e030e66303ca429aa713c47075126f5de72349e05d

Observation e7db27d3-0411-48df-acbf-67493bbd3b70 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 73

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no resolver link, observed 2026-08-15T23:12:39.063116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.063116Z digest=sha256:a855b7ecf43b1c102109487372d4c4416d65348d0b8f52ab6f5a2e802bbc63cf

Observation bbb35231-2c5d-4203-92fa-78081bb0df42 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Finetuned Language Models Are Zero-Shot Learners

Reference 74

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no resolver link, observed 2026-08-15T23:12:39.067613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.067613Z digest=sha256:8c2712d799487d91dd2ed9cae16c945b49e0474deb2186af9966b0abdbf5d00c

Observation 584bc4bf-ec0b-44ab-a104-6e2eed05cd83 · outbound

This paper cites QuRating: Selecting High-Quality Data for Training Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection QuRating: Selecting High-Quality Data for Training Language Models

Reference 75

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no resolver link, observed 2026-08-15T23:12:39.071938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.071938Z digest=sha256:dfaf1cf6e0f32ec152839d2c4aad1cce9fd4bc73df9a2f3932df93aaa3feb060

Observation 2fca783e-3bdc-445d-9915-86eaa1797c73 · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 76

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no resolver link, observed 2026-08-15T23:12:39.076022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.076022Z digest=sha256:b5934bb2250d40fdcce64f7d14b1da747ca113034cc8332d6e20804d137ce653

Observation eedf6dc9-6096-4f29-ad56-dff810550505 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 77

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no resolver link, observed 2026-08-15T23:12:39.080370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.080370Z digest=sha256:618194523fe65251d995b4b619efd9c8be30480d057679bbd1718434a6b13c33

Observation 66c43b85-efa9-4126-8ec1-938a25f8e8b4 · outbound

This paper cites Misconfidence-based Demonstration Selection for LLM In-Context Learning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Misconfidence-based Demonstration Selection for LLM In-Context Learning

Reference 78

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unresolved
no resolver link, observed 2026-08-15T23:12:39.084391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.084391Z digest=sha256:cf3b44096801f6f7c32e3629f802004c3dc2720049784302759fa62e5200cff2

Observation b50078e1-c283-4b8e-a3a7-668380413fa4 · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Reference 79

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no resolver link, observed 2026-08-15T23:12:39.088951Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:39.088951Z digest=sha256:b304ace50653182fd82bb5e5449b3f38ab38d566df84dc5a0fdedd273d8e756a

Observation cb294da4-7713-427b-b91b-1a8613bf3951 · outbound

This paper cites Qwen2.5 Technical Report.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Qwen2.5 Technical Report

Reference 80

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no resolver link, observed 2026-08-15T23:12:39.092970Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:39.092970Z digest=sha256:f70f3ff8cb94c851182c0c1520a30d92f0260f5410286f029a71eb05e115afc8

Observation 9ec26e6f-1e6c-4d73-a55a-6e1ea3143775 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 81

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unresolved
no resolver link, observed 2026-08-15T23:12:39.097196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.097196Z digest=sha256:6c498f1fee88dcdbc1a1cd716b68b6fd26d1ae0f2211604bb2149b12f9cc1470

Observation 46ace025-a2e7-42f1-8af9-6f5bdda3b375 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 82

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unresolved
no resolver link, observed 2026-08-15T23:12:39.101727Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:39.101727Z digest=sha256:4ebe148f095a865455ffcc9290210fe9fb3e75b2c7ff8f391ac2f231cf35d039

Observation fa21dab5-b260-4aa1-bddc-e5c6e784f221 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 83

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unresolved
raw_fallback, observed 2026-08-15T23:12:40.082831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:39.106949Z digest=sha256:5d60749d46633f172a0846b1678697d5eeb71fceafde504c67e209ecdfe35cbd

Observation 887df879-e716-48ce-bac4-242293fdfb35 · outbound

This paper cites TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data

Reference 84

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unresolved
no resolver link, observed 2026-08-15T23:12:39.111329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.111329Z digest=sha256:03174c16b27c6909f9b2e13bd463e79a297379f8ba64c633603ad474a916cfa6

Observation 6fa00a54-2085-4e70-96c0-7a81465244e8 · outbound

This paper cites RECOST: External Knowledge Guided Data-efficient Instruction Tuning.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection RECOST: External Knowledge Guided Data-efficient Instruction Tuning

Reference 85

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verified exact
local_arxiv, observed 2026-08-15T23:12:39.255277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:39.116100Z digest=sha256:dae1aa2f035357952c97d5f4e259e2b45e474f07849ff618ca23404eb3b439b2

Observation c60092ef-1aa3-48cc-ad72-e0c30d16e970 · outbound

This paper cites Can We Edit Factual Knowledge by In-Context Learning?.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Can We Edit Factual Knowledge by In-Context Learning?

Reference 86

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unresolved
no resolver link, observed 2026-08-15T23:12:39.121134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.121134Z digest=sha256:732a8a679888b6b324548719b57daa30e5909bc715a92cc2943d4205b8365c42

Observation a7f5f14a-8704-4f97-baef-c353a9773a42 · outbound

This paper cites LIMA: Less Is More for Alignment.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection LIMA: Less Is More for Alignment

Reference 87

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no resolver link, observed 2026-08-15T23:12:39.126315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.126315Z digest=sha256:a22bb20f9f644e23d7b20ddd0c31d3aff63e4e663b93f45efaaf6e3d42f5a682

Observation 01026809-be80-487b-9522-e6498863a0b2 · outbound

This paper cites an unresolved cited work.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Unresolved cited work

Reference 88

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unresolved
raw_fallback, observed 2026-08-15T23:12:40.068458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T23:12:39.131035Z digest=sha256:a432ae07fb180233bc1125fa7fcdb5dab4507bff32dff5ad09b0adb025042987

Observation 59b0f2b4-20e0-47c0-b241-c78ab00b6f75 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Instruction-Following Evaluation for Large Language Models

Reference 89

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.135986Z digest=sha256:58f2cd5bff7e3b425705b0fd005d6bc549d6410dafd77189012235a432635e61

Observation 961ad368-92a1-415d-9c13-da40c9d55c93 · outbound

This paper cites online" 'onlinestring :=.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection online" 'onlinestring :=

Reference 90

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no resolver link, observed 2026-08-15T23:12:39.140877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:12:39.140877Z digest=sha256:0208d12dcac5fd328e392e1f33684db77de9600307296f020484b5f734645de7

Observation 64c27ee1-595b-4c26-92a6-2ffa908191da · outbound

This paper cites write newline.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection write newline

Reference 91

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no resolver link, observed 2026-08-15T23:12:39.145675Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T23:12:39.145675Z digest=sha256:69868eb286549be9d4dc02e04d7e0490dc1f98fe52811e06c4ee051e89fef12e

Pith citing papers

No inbound Pith citation observations are available.