Paper Citation Record · LEDGER

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

As of 10 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2510.06048.

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

pith.paper-citation-record.v1
2510.06048 v5

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:16:15.616086Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T21:52:11.990088Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T23:39:04.471904Z

Reference resolution

72 of 72 outbound references displayed

verified exact0
verified fuzzy0
unresolved72
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 757ac659-f1ca-4db5-8858-a7e049df5862 · outbound

This paper cites write newline.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining write newline

Reference 1

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Observation a578da8f-7bdf-432b-b468-a9c935287318 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.290510Z

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Observation f91c319a-0584-4889-b92e-e26da5fb40f0 · outbound

This paper cites Efficient online data mixing for language model pre-training.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Efficient online data mixing for language model pre-training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.466665Z

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Observation 4b9b1bc8-d135-40f5-8e9f-22853406526a · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A Survey on Data Selection for Language Models

Reference 4

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Observation 612a1a84-321c-4826-aeb7-2393e11fff80 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Piqa: Reasoning about physical commonsense in natural language

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.737036Z

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Observation 8b58b62a-f9d0-4e13-b4b1-bfb0b55a59ed · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Coresets via bilevel optimization for continual learning and streaming

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.829736Z

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Observation c6b4d9e0-aadf-4253-b802-3838fb7d1bf1 · outbound

This paper cites Mathematical programs with optimization problems in the constraints.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Mathematical programs with optimization problems in the constraints

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.917407Z

Source-reported events for the cited work

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Observation e5499762-2eb8-4ae9-8831-89975c3dec95 · outbound

This paper cites an unresolved cited work.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:07.993881Z

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Observation 484bad9f-edc7-4a4c-bf07-fa92c45d9e80 · outbound

This paper cites On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis

Reference 9

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Observation d2cf0e28-295b-4d38-b74f-477bad2dd297 · outbound

This paper cites Skill-it! a data-driven skills framework for understanding and training language models.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Skill-it! a data-driven skills framework for understanding and training language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:08.254900Z

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Observation 11cd19dc-63bc-448f-8106-08030b86b7e9 · outbound

This paper cites Palm: Scaling language modeling with pathways.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Palm: Scaling language modeling with pathways

Reference 11

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Observation 583aa7e4-0ffc-493f-bbc2-e3070d3830b2 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:08.389415Z

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Observation d8e9fec0-1c8d-4b2f-9054-ba15b86d1860 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:08.507939Z

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Observation 4cd3fe17-5704-4431-add8-f820d3599e3f · outbound

This paper cites Cross-lingual language model pretraining.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Cross-lingual language model pretraining

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:08.620919Z

Source-reported events for the cited work

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Observation fea525a0-363d-443a-a86a-fe44b8267821 · outbound

This paper cites Detection of influential observation in linear regression.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Detection of influential observation in linear regression

Reference 15

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Observation 3c458f30-5197-4433-aa31-d301d2046384 · outbound

This paper cites A framework for bilevel optimization that enables stochastic and global variance reduction algorithms.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

Reference 16

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Observation 7a9eb371-247c-4e9a-b373-2ba21e667274 · outbound

This paper cites Foundations of bilevel programming.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Foundations of bilevel programming

Reference 17

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Observation 0f1a3899-b6ee-4242-a4ea-723ae440b09c · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Glam: Efficient scaling of language models with mixture-of-experts

Reference 18

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Observation 5577f47a-9b6c-464b-83d9-3e5e31a6150a · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining What's In My Big Data?

Reference 19

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Observation 385ed274-c754-4dbf-865f-e158358dc084 · outbound

This paper cites DsDm: Model-Aware Dataset Selection with Datamodels.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining DsDm: Model-Aware Dataset Selection with Datamodels

Reference 20

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Observation 95db6ff2-39a0-4749-87cb-42cec357cb8d · outbound

This paper cites DoGE: Domain Reweighting with Generalization Estimation.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining DoGE: Domain Reweighting with Generalization Estimation

Reference 21

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Observation fab9202e-8762-42b8-894e-821d03faea58 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Model-agnostic meta-learning for fast adaptation of deep networks

Reference 22

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Observation 22e51c21-52df-4ba0-94db-a3cd17d4aaa1 · outbound

This paper cites Bilevel programming for hyperparameter optimization and meta-learning.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel programming for hyperparameter optimization and meta-learning

Reference 23

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Observation 4a7aacb3-756c-4252-8666-da69d8dd6140 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 24

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Observation 6db1d171-de8c-4738-b852-5447e7072de2 · outbound

This paper cites A framework for few-shot language model evaluation.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A framework for few-shot language model evaluation

Reference 25

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Observation c92fb97f-847c-4c73-9429-dfac7f273f20 · outbound

This paper cites Approximation Methods for Bilevel Programming.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Approximation Methods for Bilevel Programming

Reference 26

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This paper cites A nearly optimal single loop algorithm for stochastic bilevel optimization under unbounded smoothness.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A nearly optimal single loop algorithm for stochastic bilevel optimization under unbounded smoothness

Reference 27

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Observation 394a398a-d6f4-44cb-b4f5-0fd97c6f517f · outbound

This paper cites Gemini API Additional Terms of Service , 2024.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Gemini API Additional Terms of Service , 2024

Reference 28

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Observation d69f1d4b-5867-4d81-9425-00ed39ed48f3 · outbound

This paper cites Bilevel optimization to learn training distributions for language modeling under domain shift.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel optimization to learn training distributions for language modeling under domain shift

Reference 29

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Observation eb53289c-46b0-43d5-982a-caa340d00ac7 · outbound

This paper cites Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start

Reference 30

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Observation 25ad734b-1e6c-458b-82e2-049a21769186 · outbound

This paper cites The influence curve and its role in robust estimation.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining The influence curve and its role in robust estimation

Reference 31

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Observation e4fe46b5-e24f-46f0-aacf-0c28a987fa64 · outbound

This paper cites Bilevel coreset selection in continual learning: A new formulation and algorithm.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel coreset selection in continual learning: A new formulation and algorithm

Reference 32

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel optimization under unbounded smoothness: A new algorithm and convergence analysis

Reference 33

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Observation 33356ee3-c9f4-4e08-95ca-e4c9c4a1fdd9 · outbound

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic

Reference 34

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining LoRA: Low-Rank Adaptation of Large Language Models

Reference 35

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bilevel optimization: Convergence analysis and enhanced design

Reference 36

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This paper cites Understanding black-box predictions via influence functions.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Understanding black-box predictions via influence functions

Reference 37

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining A fully first-order method for stochastic bilevel optimization

Reference 38

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This paper cites The bigscience roots corpus: A 1.6 tb composite multilingual dataset.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining The bigscience roots corpus: A 1.6 tb composite multilingual dataset

Reference 39

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Observation 49e28c09-1bff-4788-b92a-0e0126d8752d · outbound

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Deduplicating Training Data Makes Language Models Better

Reference 40

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Observation 7646492c-b6e3-4dec-a8c1-fb7e792237b0 · outbound

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining DataComp-LM: In search of the next generation of training sets for language models

Reference 41

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Residuals and influence in regression, 1984

Reference 42

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 43

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 45

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining OpenAI Terms of Service , 2024

Reference 46

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Distributionally Robust Language Modeling

Reference 47

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting

Reference 48

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Observation a94bc637-ebd3-4d44-9da7-e1e30e9e8057 · outbound

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining TRAK: Attributing Model Behavior at Scale

Reference 50

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 51

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 52

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Observation 12a36c9f-d06d-416a-b29b-9c128d9835a2 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 53

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 54

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Observation 1379fcf6-0f1d-41f7-bc6f-6b249153bed4 · outbound

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Distributionally robust neural networks

Reference 55

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BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Winogrande: An adversarial winograd schema challenge at scale

Reference 56

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Observation a596660d-a762-45e5-b50d-ae0096d1f69e · outbound

This paper cites SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection

Reference 57

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Observation 4fa88c9f-fd48-48bd-a7fe-96db4088d201 · outbound

This paper cites Bi-level finetuning with task-dependent similarity structure for low-resource training.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Bi-level finetuning with task-dependent similarity structure for low-resource training

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.079094Z

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Observation 32a93d2e-98d6-466a-a0ae-9c6bc8ad36df · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Beyond neural scaling laws: beating power law scaling via data pruning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.150775Z

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Observation f5c07352-53f7-4b0f-9bb9-d873cb3e5ad2 · outbound

This paper cites D4: Improving llm pretraining via document de-duplication and diversification.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining D4: Improving llm pretraining via document de-duplication and diversification

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.247390Z

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Observation 298b50c8-98f7-4132-b482-8bdb947bf6a0 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Crowdsourcing Multiple Choice Science Questions

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.372420Z

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Observation e31f17c5-f02a-4005-8b4d-9c7ae6b0fe08 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.514772Z

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Observation f71f9222-a991-4346-b00f-6b81fd770bdf · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining QuRating: Selecting High-Quality Data for Training Language Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.661866Z

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Observation 31c7523f-c90b-4089-9984-eb0b5fc3b805 · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.810456Z

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Observation a3b348a0-105a-462f-b5a8-f6b9c5595c07 · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:14.978521Z

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Observation 29d7d1d5-a55e-4fb2-9e6d-d5438fcf7322 · outbound

This paper cites Doremi: Optimizing data mixtures speeds up language model pretraining.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Doremi: Optimizing data mixtures speeds up language model pretraining

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.069374Z

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Observation f2e6c024-310e-4421-a8fc-e84dc14d8159 · outbound

This paper cites Data selection for language models via importance resampling.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Data selection for language models via importance resampling

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.201535Z

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Observation 0ac98bfb-6693-4547-848f-59cec9b21172 · outbound

This paper cites MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.281996Z

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Observation 2f41d391-b46f-4a2d-a140-62234a846f0a · outbound

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

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.368440Z

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Observation 281e5d82-efa4-4f0b-aadc-0c128dc7dc89 · outbound

This paper cites Probabilistic bilevel coreset selection.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Probabilistic bilevel coreset selection

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.433553Z

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Observation 0a2e9b1a-ee6f-426c-b8d3-0a146cfca2e8 · outbound

This paper cites @esa (Ref.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining @esa (Ref

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.483111Z

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Observation 6c9e1b97-73ec-49ee-9762-1b9f826640a6 · outbound

This paper cites an unresolved cited work.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining Unresolved cited work

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.549337Z

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Observation 170a26b8-54f1-4e44-a3f6-62a0fbcef3e1 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T11:16:15.616086Z

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Pith citing papers

Observation a8529f82-f5b4-4e97-aa22-9ebdbaeae50f · inbound

Let the Target Select for Itself: Data Selection via Target-Aligned Paths cites this paper.

Let the Target Select for Itself: Data Selection via Target-Aligned Paths BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-02T03:04:01.491891Z

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Observation ed7cae44-2241-45e7-8ad9-711b34efab79 · inbound

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training cites this paper.

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-03T23:39:04.474122Z

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