LLM generation novelty through the lens of semantic similarity | Paper Citation Record

Observation 7057e73c-ff38-4ddb-b10a-56800f576849 · outbound

This paper cites Towards tracing knowledge in language models back to the training data.

LLM generation novelty through the lens of semantic similarity Towards tracing knowledge in language models back to the training data

Reference 1

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Observation 7f038324-75d4-4b73-b3b2-c76b90d0f3f1 · outbound

This paper cites Smollm-blazingly fast and remarkably powerful.

LLM generation novelty through the lens of semantic similarity Smollm-blazingly fast and remarkably powerful

Reference 2

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Observation 35f6c891-fe8d-4d93-8836-898eaf4272b8 · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

LLM generation novelty through the lens of semantic similarity SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 3

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Observation 63354f86-9774-4da2-b903-bc4f1837d279 · outbound

This paper cites If influence functions are the answer, then what is the question? Advances in Neural Information Processing Systems, 35: 0 17953--17967, 2022.

LLM generation novelty through the lens of semantic similarity If influence functions are the answer, then what is the question? Advances in Neural Information Processing Systems, 35: 0 17953--17967, 2022

Reference 4

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This paper cites Training data attribution via approximate unrolling.

LLM generation novelty through the lens of semantic similarity Training data attribution via approximate unrolling

Reference 5

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This paper cites Influence functions in deep learning are fragile.

LLM generation novelty through the lens of semantic similarity Influence functions in deep learning are fragile

Reference 6

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Observation ec2e9b65-39c7-4484-aaa4-b816a7a102d5 · outbound

This paper cites Quantifying memorization across neural language models.

LLM generation novelty through the lens of semantic similarity Quantifying memorization across neural language models

Reference 7

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Observation a96cdbc7-e339-4bdc-91a4-c458dc9c38b4 · outbound

This paper cites Scalable influence and fact tracing for large language model pretraining.

LLM generation novelty through the lens of semantic similarity Scalable influence and fact tracing for large language model pretraining

Reference 8

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Observation 040c75cf-a3d6-4bb2-9784-bd68fe2671aa · outbound

This paper cites What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

LLM generation novelty through the lens of semantic similarity What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Reference 9

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Observation e4b58db7-455c-47c1-a886-aa56d6b8c92c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

LLM generation novelty through the lens of semantic similarity Training Verifiers to Solve Math Word Problems

Reference 10

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Observation 4c49077d-d6f4-4060-8ac8-b786c58b9285 · outbound

This paper cites an unresolved cited work.

LLM generation novelty through the lens of semantic similarity Unresolved cited work

Reference 11

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This paper cites The Faiss library.

LLM generation novelty through the lens of semantic similarity The Faiss library

Reference 12

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Observation 76b5ece1-fbf5-479d-9e12-0ed11568203b · outbound

This paper cites Mmteb: Massive multilingual text embedding benchmark.

LLM generation novelty through the lens of semantic similarity Mmteb: Massive multilingual text embedding benchmark

Reference 13

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Observation 7f6512ee-ca37-45ff-9cdf-c6c86f179395 · outbound

This paper cites Revisiting the fragility of influence functions.

LLM generation novelty through the lens of semantic similarity Revisiting the fragility of influence functions

Reference 14

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Observation a5007c6e-2973-4308-94e3-a09c20c1ad8c · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

LLM generation novelty through the lens of semantic similarity What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 15

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Observation 83933c59-d09f-4010-b430-5b7555497179 · outbound

This paper cites The language model evaluation harness, 07 2024.

LLM generation novelty through the lens of semantic similarity The language model evaluation harness, 07 2024

Reference 16

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This paper cites A closer look at the limitations of instruction tuning.

LLM generation novelty through the lens of semantic similarity A closer look at the limitations of instruction tuning

Reference 17

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This paper cites LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations.

LLM generation novelty through the lens of semantic similarity LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Reference 18

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Observation 461b36d8-226a-4d1b-abb1-df2ce0f1774d · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

LLM generation novelty through the lens of semantic similarity Studying Large Language Model Generalization with Influence Functions

Reference 19

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Observation 131b9465-304e-4455-8292-cb760feb1186 · outbound

This paper cites Fastif: Scalable influence functions for efficient model interpretation and debugging.

LLM generation novelty through the lens of semantic similarity Fastif: Scalable influence functions for efficient model interpretation and debugging

Reference 20

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Observation 53b20509-aeb8-4a65-bf96-5cd9742f8c8c · outbound

This paper cites Lighteval: A lightweight framework for llm evaluation, 2023.

LLM generation novelty through the lens of semantic similarity Lighteval: A lightweight framework for llm evaluation, 2023

Reference 21

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Observation 6ac03696-b350-4c37-af00-40c497c1bca7 · outbound

This paper cites Training data influence analysis and estimation: a survey.

LLM generation novelty through the lens of semantic similarity Training data influence analysis and estimation: a survey

Reference 22

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Observation 02caca92-0bde-415e-9bce-7b53fcb0b00a · outbound

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

LLM generation novelty through the lens of semantic similarity The influence curve and its role in robust estimation

Reference 23

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Observation 4eb12f7c-1cc5-4817-934a-1a23760d773a · outbound

This paper cites Data cleansing for models trained with sgd.

LLM generation novelty through the lens of semantic similarity Data cleansing for models trained with sgd

Reference 24

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Observation 2bef80c5-9478-488e-a413-fe6fc6283ae2 · outbound

This paper cites Most influential subset selection: Challenges, promises, and beyond.

LLM generation novelty through the lens of semantic similarity Most influential subset selection: Challenges, promises, and beyond

Reference 25

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Observation 589b7f10-d0f3-4193-a0e8-645a5b32c470 · outbound

This paper cites MAGIC: Near-Optimal Data Attribution for Deep Learning.

LLM generation novelty through the lens of semantic similarity MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 26

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Observation 08c3f432-415e-4edb-860f-d4c4a5b54425 · outbound

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

LLM generation novelty through the lens of semantic similarity Understanding black-box predictions via influence functions

Reference 27

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

LLM generation novelty through the lens of semantic similarity Rouge: A package for automatic evaluation of summaries

Reference 28

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Observation 6391549b-e3f9-478d-9648-b27916ef2a8c · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

LLM generation novelty through the lens of semantic similarity Truthfulqa: Measuring how models mimic human falsehoods

Reference 29

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Observation bf831371-c2d7-4d56-9a17-20d0d8f0e699 · outbound

This paper cites OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens.

LLM generation novelty through the lens of semantic similarity OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens

Reference 30

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Observation e0264989-7d06-43ef-9954-c1e87a805a77 · outbound

This paper cites Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens.

LLM generation novelty through the lens of semantic similarity Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens

Reference 31

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Observation 1745a09c-6c79-4b38-9c5f-01be15b4e9f6 · outbound

This paper cites How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven.

LLM generation novelty through the lens of semantic similarity How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven

Reference 32

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This paper cites Evaluating n-gram novelty of language models using rusty-dawg.

LLM generation novelty through the lens of semantic similarity Evaluating n-gram novelty of language models using rusty-dawg

Reference 33

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This paper cites Waka: Data attribution using k-nearest neighbors and membership privacy principles.

LLM generation novelty through the lens of semantic similarity Waka: Data attribution using k-nearest neighbors and membership privacy principles

Reference 34

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This paper cites Mteb: Massive text embedding benchmark.

LLM generation novelty through the lens of semantic similarity Mteb: Massive text embedding benchmark

Reference 35

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This paper cites A bayesian approach to analysing training data attribution in deep learning.

LLM generation novelty through the lens of semantic similarity A bayesian approach to analysing training data attribution in deep learning

Reference 36

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This paper cites Trak: Attributing model behavior at scale.

LLM generation novelty through the lens of semantic similarity Trak: Attributing model behavior at scale

Reference 37

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This paper cites Near-duplicate sequence search at scale for large language model memorization evaluation.

LLM generation novelty through the lens of semantic similarity Near-duplicate sequence search at scale for large language model memorization evaluation

Reference 38

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Observation 31c86b42-3a1e-4292-a232-796566de75c2 · outbound

This paper cites Estimating training data influence by tracing gradient descent.

LLM generation novelty through the lens of semantic similarity Estimating training data influence by tracing gradient descent

Reference 39

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This paper cites Scaling up membership inference: When and how attacks succeed on large language models.

LLM generation novelty through the lens of semantic similarity Scaling up membership inference: When and how attacks succeed on large language models

Reference 40

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

LLM generation novelty through the lens of semantic similarity Learning or self-aligning? rethinking instruction fine-tuning

Reference 41

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This paper cites Colbertv2: Effective and efficient retrieval via lightweight late interaction.

LLM generation novelty through the lens of semantic similarity Colbertv2: Effective and efficient retrieval via lightweight late interaction

Reference 42

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This paper cites Scaling up influence functions.

LLM generation novelty through the lens of semantic similarity Scaling up influence functions

Reference 43

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This paper cites Rewritelm: an instruction-tuned large language model for text rewriting.

LLM generation novelty through the lens of semantic similarity Rewritelm: an instruction-tuned large language model for text rewriting

Reference 44

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This paper cites GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning.

LLM generation novelty through the lens of semantic similarity GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

Reference 45

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This paper cites pes2o (pretraining efficiently on s2orc) dataset.

LLM generation novelty through the lens of semantic similarity pes2o (pretraining efficiently on s2orc) dataset

Reference 46

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

LLM generation novelty through the lens of semantic similarity Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Reference 47

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This paper cites Enhancing training data attribution with representational optimization, 2025.

LLM generation novelty through the lens of semantic similarity Enhancing training data attribution with representational optimization, 2025

Reference 48

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This paper cites Better Training Data Attribution via Better Inverse Hessian-Vector Products.

LLM generation novelty through the lens of semantic similarity Better Training Data Attribution via Better Inverse Hessian-Vector Products

Reference 49

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This paper cites Capturing the temporal dependence of training data influence.

LLM generation novelty through the lens of semantic similarity Capturing the temporal dependence of training data influence

Reference 50

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Observation 1cc1e034-90b1-4c14-8af9-df8117f01c3c · outbound

This paper cites Generalization vs memorization: Tracing language models’ capabilities back to pretraining data.

LLM generation novelty through the lens of semantic similarity Generalization vs memorization: Tracing language models’ capabilities back to pretraining data

Reference 51

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This paper cites Memhunter: Automated and verifiable memorization detection at dataset-scale in llms, 2025.

LLM generation novelty through the lens of semantic similarity Memhunter: Automated and verifiable memorization detection at dataset-scale in llms, 2025

Reference 52

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This paper cites Less: Selecting influential data for targeted instruction tuning.

LLM generation novelty through the lens of semantic similarity Less: Selecting influential data for targeted instruction tuning

Reference 53

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This paper cites Representer point selection for explaining deep neural networks.

LLM generation novelty through the lens of semantic similarity Representer point selection for explaining deep neural networks

Reference 54

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This paper cites Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models.

LLM generation novelty through the lens of semantic similarity Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models

Reference 55

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This paper cites Pretraining data detection for large language models: A divergence-based calibration method.

LLM generation novelty through the lens of semantic similarity Pretraining data detection for large language models: A divergence-based calibration method

Reference 56

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This paper cites Dense text retrieval based on pretrained language models: A survey.

LLM generation novelty through the lens of semantic similarity Dense text retrieval based on pretrained language models: A survey

Reference 57

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This paper cites @esa (Ref.

LLM generation novelty through the lens of semantic similarity @esa (Ref

Reference 58

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LLM generation novelty through the lens of semantic similarity Unresolved cited work

Reference 59

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This paper cites Most training‑data attribution (TDA) methods ask which training examples causally influence a given output, often using leave‑one‑out tests.

LLM generation novelty through the lens of semantic similarity Most training‑data attribution (TDA) methods ask which training examples causally influence a given output, often using leave‑one‑out tests

Reference 60

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