Paper Citation Record · LEDGER

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success

As of 15 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.01992.

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

pith.paper-citation-record.v1
2506.01992 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:39:28.668920Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

46 of 46 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22169253-971b-4afd-b8b3-c4c8cc8096f5 · outbound

This paper cites Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal

Reference 1

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This paper cites Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:29.148963Z

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This paper cites Is margin all you need? An extensive empirical study of active learning on tabular data.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Is margin all you need? An extensive empirical study of active learning on tabular data

Reference 3

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This paper cites Language Models are Few-Shot Learners.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Language Models are Few-Shot Learners

Reference 4

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This paper cites Emerging properties in self-supervised vision transformers.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Emerging properties in self-supervised vision transformers

Reference 5

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Observation e66464e0-3fbe-4719-8e77-68fbdb282f55 · outbound

This paper cites Making your first choice: To address cold start problem in medical active learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Making your first choice: To address cold start problem in medical active learning

Reference 6

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Observation aa288c20-10aa-407f-a094-82ced820a4f5 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:29.105452Z

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This paper cites Revisiting active learning in the era of vision foundation models.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Revisiting active learning in the era of vision foundation models

Reference 8

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This paper cites Active learning on a budget: Opposite strategies suit high and low budgets.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active learning on a budget: Opposite strategies suit high and low budgets

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:29.085255Z

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Observation bd6dd026-e51a-48b6-ba86-3bbe04662991 · outbound

This paper cites A Framework and Benchmark for Deep Batch Active Learning for Regression.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success A Framework and Benchmark for Deep Batch Active Learning for Regression

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:29.075754Z

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Observation 53f07206-1158-4645-87ac-ea7b6d57ac38 · outbound

This paper cites Fast fishing: Approximating bait for efficient and scalable deep active image classification.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Fast fishing: Approximating bait for efficient and scalable deep active image classification

Reference 11

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Observation af3ddee9-2215-460b-9b86-65475a50c745 · outbound

This paper cites The interplay of uncertainty modeling and deep active learning: An empirical analysis in image classification.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success The interplay of uncertainty modeling and deep active learning: An empirical analysis in image classification

Reference 12

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This paper cites Randomness is the root of all evil: more reliable evaluation of deep active learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Randomness is the root of all evil: more reliable evaluation of deep active learning

Reference 13

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Observation afa62ff1-5ae6-4ca5-a710-efce1eb7a203 · outbound

This paper cites LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages

Reference 14

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This paper cites scikit-activeml: A library and toolbox for active learning algorithms.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success scikit-activeml: A library and toolbox for active learning algorithms

Reference 15

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Observation ddf09ac3-cfc0-4ef4-9f68-6e5752f27a79 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 16

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This paper cites Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment

Reference 17

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This paper cites On the importance of effectively adapting pretrained language models for active learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success On the importance of effectively adapting pretrained language models for active learning

Reference 18

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This paper cites ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

Reference 19

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This paper cites Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024

Reference 20

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success MTEB: Massive text embedding benchmark

Reference 21

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This paper cites Towards Robust and Reproducible Active Learning Using Neural Networks.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Towards Robust and Reproducible Active Learning Using Neural Networks

Reference 22

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success GPT-4 Technical Report

Reference 23

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Unresolved cited work

Reference 24

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This paper cites Pedregosa, G.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Pedregosa, G

Reference 25

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This paper cites Improving language understanding by generative pre-training, 2018.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Improving language understanding by generative pre-training, 2018

Reference 26

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This paper cites Language models are unsupervised multitask learners, 2019.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Language models are unsupervised multitask learners, 2019

Reference 27

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Activeglae: A benchmark for deep active learning with transformers

Reference 28

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Towards Deep Active Learning in Avian Bioacoustics

Reference 29

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Sentence-bert: Sentence embeddings using siamese bert- networks

Reference 30

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This paper cites Revisiting uncertainty-based query strategies for active learning with transformers.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Revisiting uncertainty-based query strategies for active learning with transformers

Reference 31

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active learning for convolutional neural networks: A core-set approach

Reference 32

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active learning literature survey

Reference 33

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active learning helps pretrained models learn the intended task

Reference 34

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This paper cites LLMs are Also Effective Embedding Models: An In-depth Overview.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success LLMs are Also Effective Embedding Models: An In-depth Overview

Reference 35

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Qwen2.5 Technical Report

Reference 36

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Attention is All you Need

Reference 37

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This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 38

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success A Cross-Domain Benchmark for Active Learning

Reference 39

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success C-pack: Packed resources for general chinese embeddings

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:28.834498Z

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This paper cites Active learning through a covering lens.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Active learning through a covering lens

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:28.823763Z

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This paper cites Cold-start active learning through self-supervised language modeling.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Cold-start active learning through self-supervised language modeling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:28.813656Z

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This paper cites A Comparative Survey: Benchmarking for Pool-based Active Learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success A Comparative Survey: Benchmarking for Pool-based Active Learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:28.802638Z

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Observation d3d6d7ca-35cd-422c-8547-4d51d41679a1 · outbound

This paper cites A Comparative Survey of Deep Active Learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success A Comparative Survey of Deep Active Learning

Reference 44

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No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success Jasper and Stella: distillation of SOTA embedding models

Reference 45

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This paper cites LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning.

No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:28.792031Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.