Paper Citation Record · LEDGER

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain

As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2505.14826.

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

pith.paper-citation-record.v1
2505.14826 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:33:55.702118Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T09:19:39.848194Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T09:21:21.452805Z

Reference resolution

51 of 51 outbound references displayed

verified exact2
verified fuzzy24
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcdd67a7-9a4f-48fe-a4fb-2511c49dbc64 · outbound

This paper cites write newline.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain write newline

Reference 1

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Source-reported events for the cited work

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Observation 424a77be-2830-4c67-badd-cb43a352dbb6 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:49.406344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7fa811c-abfe-4f18-866b-5bcee96213db · outbound

This paper cites Improved algorithms for linear stochastic bandits.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Improved algorithms for linear stochastic bandits

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:03.803499Z

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Observation 9d299939-1bc1-4683-bf7a-e12260681d37 · outbound

This paper cites P., and Wunder, M.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain P., and Wunder, M

Reference 4

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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 10456815-691d-4ac8-8d51-eb7b5e19b2cf · outbound

This paper cites an unresolved cited work.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation 2b3129c8-81ee-493d-ab6c-ba184d858840 · outbound

This paper cites Pattern Recognition and Machine Learning.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Pattern Recognition and Machine Learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:03.036966Z

Source-reported events for the cited work

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Observation 440e0be3-022f-4413-ace6-9fee82a33c73 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain On the Opportunities and Risks of Foundation Models

Reference 7

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Observation a2b9c314-426e-4062-9f3f-45f48fb1accc · outbound

This paper cites Coresets via bilevel optimization for continual learning and streaming.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Coresets via bilevel optimization for continual learning and streaming

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:02.789773Z

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 b21aa86a-3b3f-44c6-bf34-cd3a21d39834 · outbound

This paper cites an unresolved cited work.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 9

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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 43649b05-d5d4-46ca-a0c7-d1c2ffd0d77f · outbound

This paper cites Super-Samples from Kernel Herding.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Super-Samples from Kernel Herding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:50.595074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f747e9-5d66-46c1-b1db-1038e62a893a · outbound

This paper cites M., Haussmann, E., and Fardet, E.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain M., Haussmann, E., and Fardet, E

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:02.258315Z

Source-reported events for the cited work

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Observation 14c92296-4c4d-42b5-8064-04250ade4892 · outbound

This paper cites and Shrivastava, A.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain and Shrivastava, A

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65ea28c3-3bc3-459f-84d4-69e99929c55e · outbound

This paper cites Selection via proxy: Efficient data selection for deep learning.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Selection via proxy: Efficient data selection for deep learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:02.038794Z

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 fc84104b-b3ff-4fd6-aee8-135c2a4557f2 · outbound

This paper cites Active Preference Optimization for Sample Efficient RLHF.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Active Preference Optimization for Sample Efficient RLHF

Reference 14

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Source-reported events for the cited work

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Observation 66056af6-47f9-4bbb-b5bb-6a1465fbfc34 · outbound

This paper cites and Zhang, C.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain and Zhang, C

Reference 15

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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 7de814da-df92-4acb-bb0f-ea1e5cc4ea01 · outbound

This paper cites On the mathematical foundations of theoretical statistics.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain On the mathematical foundations of theoretical statistics

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:01.466564Z

Source-reported events for the cited work

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Observation 3b88b5e0-1bfd-4e8f-b11c-5dd4c87ee52b · outbound

This paper cites Minimax-optimal Inference from Partial Rankings.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Minimax-optimal Inference from Partial Rankings

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:33:56.609259Z

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 c516dfb3-4f8d-464c-ad70-4383042b3d25 · outbound

This paper cites an unresolved cited work.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9950644-7869-4f40-9e56-0798500eed05 · outbound

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

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain LoRA : Low-rank adaptation of large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:01.254404Z

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 f930385d-710a-4e19-88fa-ecf1990b1214 · outbound

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FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 20223e23-d6a5-494f-b997-e27c74baff71 · outbound

This paper cites and Liberty, E.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain and Liberty, E

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:00.933462Z

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 288ac579-d70f-4519-a0cf-39f96417ac91 · outbound

This paper cites char-rnn.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain char-rnn

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:00.669480Z

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 3dc3c016-1e45-4bd0-9568-9e1fa1bc6a41 · outbound

This paper cites and Szepesvari, C.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain and Szepesvari, C

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:00.446532Z

Source-reported events for the cited work

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Observation c632ca69-546e-4b6a-86ea-8a88854e1ca4 · outbound

This paper cites Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Reference 24

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Source-reported events for the cited work

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Observation c715a6c3-925b-4021-87a9-c8303ad1fe6d · outbound

This paper cites Deduplicating training data makes language models better.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Deduplicating training data makes language models better

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:00.165111Z

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 11e2c045-38c1-4fd7-85f6-88d51aca9b29 · outbound

This paper cites Dual Active Learning for Reinforcement Learning from Human Feedback.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Dual Active Learning for Reinforcement Learning from Human Feedback

Reference 26

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Source-reported events for the cited work

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Observation df986964-b692-4a24-92f0-95cb5c07d13a · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 27

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Source-reported events for the cited work

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Observation 79fcef58-9e46-4540-ac9f-6617a2db6e4d · outbound

This paper cites Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond).

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:33:56.330976Z

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 b7cd40f1-9e20-4086-b05c-5f5af4492c3e · outbound

This paper cites S., and Dean, J.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain S., and Dean, J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:59.839731Z

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 1e9e8a58-a9c4-46ae-af55-da9d1fdc3dad · outbound

This paper cites Prioritized training on points that are learnable, worth learning, and not yet learnt.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Prioritized training on points that are learnable, worth learning, and not yet learnt

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:59.642934Z

Source-reported events for the cited work

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Observation 572be791-6b49-425e-bb1c-fa4610de8ccf · outbound

This paper cites The star-shaped space of solutions of the spherical negative perceptron.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain The star-shaped space of solutions of the spherical negative perceptron

Reference 31

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Source-reported events for the cited work

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Observation 4cc89c8e-a620-4d37-8b83-0ff9f53369c6 · outbound

This paper cites Optimal design for human preference elicitation.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Optimal design for human preference elicitation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:59.435562Z

Source-reported events for the cited work

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Observation de061543-acf5-46c6-9b9d-8f06fc21807a · outbound

This paper cites L., Wolsey, L.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain L., Wolsey, L

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:59.171920Z

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 77fca0d3-9b9e-4280-b16c-95e8b1e666e1 · outbound

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

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Training language models to follow instructions with human feedback

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:58.951173Z

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 b6fea7d4-6d13-46f5-8e20-86f44525c2f7 · outbound

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FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 35

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Observation 13517ba4-ba3e-442a-81ae-5d12ae33a063 · outbound

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FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 36

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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 9d6e6a10-bf62-4d2e-99a2-a92d521ce1e2 · outbound

This paper cites Optimal Design of Experiments, volume 50 of Classics in Applied Mathematics.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Optimal Design of Experiments, volume 50 of Classics in Applied Mathematics

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:58.108840Z

Source-reported events for the cited work

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Observation 94379257-47b7-4a15-8c47-503f79d20f98 · outbound

This paper cites Language models are unsupervised multitask learners.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Language models are unsupervised multitask learners

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:57.859146Z

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 db80c7d6-3f9c-4ad1-8d2e-e5bff9fed775 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Direct preference optimization: Your language model is secretly a reward model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:57.718365Z

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 05c8e0f8-fa83-4633-9b7b-aa91d74cacef · outbound

This paper cites SVP-CF: Selection via Proxy for Collaborative Filtering Data.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain SVP-CF: Selection via Proxy for Collaborative Filtering Data

Reference 40

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Observation 75691a37-0af0-4eb6-8cbb-6578d75e4297 · outbound

This paper cites How to Train Data-Efficient LLMs.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain How to Train Data-Efficient LLMs

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:54.356737Z

Source-reported events for the cited work

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Observation c58e1577-1621-4ef2-9eb0-c87452a42a56 · outbound

This paper cites Optimal Design for Reward Modeling in RLHF.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Optimal Design for Reward Modeling in RLHF

Reference 42

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Source-reported events for the cited work

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Observation f9715298-d517-417e-b192-2b2340faf1e2 · outbound

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FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Unresolved cited work

Reference 43

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Source-reported events for the cited work

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Observation 16d7da80-b2c7-4f87-8fec-eb8b86ae27cc · outbound

This paper cites and Yang, M.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain and Yang, M

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:57.419423Z

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 64413cef-0d91-4807-9ca8-f8e52ee96539 · outbound

This paper cites Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:54.880840Z

Source-reported events for the cited work

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Observation cc034977-d9fb-489d-9ff7-ad39c71ea6ce · outbound

This paper cites D4: Improving LLM Pretraining via Document De-Duplication and Diversification.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 46

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Source-reported events for the cited work

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Observation f0bc88ca-ac6d-4833-9c01-9e5f391100cd · outbound

This paper cites On coresets for support vector machines.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain On coresets for support vector machines

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:57.238907Z

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 0b35729e-5db2-4b13-8ab3-097ecd82dbef · outbound

This paper cites W., Lester, B., Du, N., Dai, A., and Le, Q.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain W., Lester, B., Du, N., Dai, A., and Le, Q

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:33:57.000752Z

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 253f9be1-f33b-44e4-8048-6bebdc1a3247 · outbound

This paper cites CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:55.441839Z

Source-reported events for the cited work

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Observation de0b357e-c288-41aa-af0f-e59281d439fe · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:55.546557Z

Source-reported events for the cited work

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Observation 8b16b78e-9cf1-419d-94fc-e7029f1716da · outbound

This paper cites Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:55.702118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6834289c-1c52-4239-8fce-1a850878b6aa · inbound

AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment cites this paper.

AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:13:16.128122Z

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AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment cites this paper.

AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:21:21.457051Z

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