Paper Citation Record · LEDGER

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 3 inbound Pith citation observations for arXiv:2505.16178.

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

pith.paper-citation-record.v1
2505.16178 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

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measured 57 of 57 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:53:40.719452Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T14:19:53.912897Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation b3b0063e-b53a-4442-9ee6-3de35ee46ad9 · outbound

This paper cites Evaluating correctness and faithfulness of instruction-following models for question answering.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Evaluating correctness and faithfulness of instruction-following models for question answering

Reference 1

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Observation 9cc26fe9-fad4-4789-b343-d91b52fa66f7 · outbound

This paper cites Llama 3.2: Revolutionizing edge ai and vision with open, customizable models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Llama 3.2: Revolutionizing edge ai and vision with open, customizable models

Reference 2

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Observation e41ee26d-26d9-4ee4-a093-efb3b83ee40f · outbound

This paper cites Physics of language models: Part 3.1, knowledge storage and extraction.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Physics of language models: Part 3.1, knowledge storage and extraction

Reference 3

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Observation 13869605-8315-40d7-b021-776c72b2b662 · outbound

This paper cites Physics of language models: Part 3.2, knowledge manipula- tion.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Physics of language models: Part 3.2, knowledge manipula- tion

Reference 4

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Observation 59b507d6-e94e-4515-be59-e36ee589cdf8 · outbound

This paper cites Towards better understanding of gradient-based attribution methods for deep neural networks.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Towards better understanding of gradient-based attribution methods for deep neural networks

Reference 5

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Observation bb00f3c9-52e9-4000-9e30-a8303963cfb8 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 6

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This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Pythia: A suite for analyzing large language models across training and scaling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.833474Z

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Observation 195992c6-268c-442d-8397-2f86538a3eff · outbound

This paper cites Instructpix2pix: Learning to follow image editing instructions.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Instructpix2pix: Learning to follow image editing instructions

Reference 8

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Observation 2360e71c-2c40-47e3-ae0a-0bc67a629fff · outbound

This paper cites Causal scrubbing: A method for rigorously testing interpretability hypotheses.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Causal scrubbing: A method for rigorously testing interpretability hypotheses

Reference 9

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Observation 6a5d02bf-22bf-4698-b299-fe3e35741a63 · outbound

This paper cites Journey to the center of the knowledge neurons: Discoveries of language-independent knowledge neurons and degenerate knowledge neurons.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Journey to the center of the knowledge neurons: Discoveries of language-independent knowledge neurons and degenerate knowledge neurons

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.790929Z

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Observation 8d2b9e9e-e811-48bf-9e9b-738bea0e7407 · outbound

This paper cites Instruc- tion pre-training: Language models are supervised multitask learners.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Instruc- tion pre-training: Language models are supervised multitask learners

Reference 11

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Observation 4c73c785-e1a6-4ccb-a084-1ae57e33ea00 · outbound

This paper cites Zhao, Yanping Huang, Andrew M.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Zhao, Yanping Huang, Andrew M

Reference 12

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Observation dd64acd9-f7d6-45cb-aa59-4472c17c6751 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Towards automated circuit discovery for mechanistic interpretability

Reference 13

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Observation 7b27cf0d-cc75-4629-9085-66d95b2803c0 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 14

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Observation 9897528a-d134-4077-9dbb-06064f5b0d74 · outbound

This paper cites Knowledge neurons in pretrained transformers.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Knowledge neurons in pretrained transformers

Reference 15

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Observation 4554704a-1992-437f-993f-6e83a25c1ccf · outbound

This paper cites InstructBLIP: Towards general-purpose vision-language models with instruction tuning.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge InstructBLIP: Towards general-purpose vision-language models with instruction tuning

Reference 16

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Observation a587e94d-0051-49f0-9fe2-5f29f904ea42 · outbound

This paper cites DeepSeek-V3 Technical Report.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge DeepSeek-V3 Technical Report

Reference 17

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This paper cites Not all lan- guage model features are one-dimensionally linear.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Not all lan- guage model features are one-dimensionally linear

Reference 18

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Observation d014f255-0e16-448a-a806-e21eaa130da2 · outbound

This paper cites Dissecting recall of factual associations in auto-regressive language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Dissecting recall of factual associations in auto-regressive language models

Reference 19

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Observation 81677c5e-c886-498b-92d1-b341063cfaa4 · outbound

This paper cites Understanding finetuning for factual knowledge extraction.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Understanding finetuning for factual knowledge extraction

Reference 20

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Observation 6525eaa8-741a-46dd-9d05-2d331af21aa8 · outbound

This paper cites Reverse training to nurse the reversal curse.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Reverse training to nurse the reversal curse

Reference 21

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Observation 0c080acd-9d5b-4cbf-9ee5-09248da88793 · outbound

This paper cites Universal neurons in GPT2 language models.Transactions on Machine Learning Research, 2024.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Universal neurons in GPT2 language models.Transactions on Machine Learning Research, 2024

Reference 22

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Observation b3a2cf25-4c7d-4583-9365-fe5ad4d26828 · outbound

This paper cites Finding neurons in a haystack: Case studies with sparse probing.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Finding neurons in a haystack: Case studies with sparse probing

Reference 23

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Observation 8bc5750b-3486-45c9-867a-40dd8d54e304 · outbound

This paper cites Language models represent space and time.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Language models represent space and time

Reference 24

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Observation 22c0e65d-3d6c-4eb0-816d-d556c3b90c6f · outbound

This paper cites Language models as knowledge bases: On entity rep- resentations, storage capacity, and paraphrased queries.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Language models as knowledge bases: On entity rep- resentations, storage capacity, and paraphrased queries

Reference 25

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This paper cites Monotonic representation of numeric attributes in language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Monotonic representation of numeric attributes in language models

Reference 26

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Observation 7e95b614-8cc0-44ea-97da-1c9198c764b0 · outbound

This paper cites Linearity of relation decoding in transformer language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Linearity of relation decoding in transformer language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.539743Z

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This paper cites Dick, Hidenori Tanaka, Tim Rocktäschel, Edward Grefenstette, and David Krueger.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Dick, Hidenori Tanaka, Tim Rocktäschel, Edward Grefenstette, and David Krueger

Reference 28

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This paper cites Instruction-tuned language models are better knowledge learners.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Instruction-tuned language models are better knowledge learners

Reference 29

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Observation 6527b1a8-f8f4-4b71-b4c3-b756d54108a3 · outbound

This paper cites Backward lens: Projecting language model gradients into the vocabulary space.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Backward lens: Projecting language model gradients into the vocabulary space

Reference 30

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Observation a4e94535-4f2c-4890-92bc-8106a40b3e26 · outbound

This paper cites The remarkable robustness of LLMs: Stages of inference? In ICML 2024 Workshop on Mechanistic Interpretability, 2024.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge The remarkable robustness of LLMs: Stages of inference? In ICML 2024 Workshop on Mechanistic Interpretability, 2024

Reference 31

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Observation 5697e7b4-3679-4e45-8f6c-e3dda5db059b · outbound

This paper cites Understanding Neural Networks through Representation Erasure.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Understanding Neural Networks through Representation Erasure

Reference 32

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This paper cites Relation also knows: Rethinking the recall and editing of factual associations in auto-regressive transformer language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Relation also knows: Rethinking the recall and editing of factual associations in auto-regressive transformer language models

Reference 33

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Observation 7608ad4a-b913-4aa0-8d5e-a3094aef8c41 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge The flan collection: Designing data and methods for effective instruction tuning

Reference 34

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This paper cites Can neural network memorization be localized? In International Conference on Machine Learning, pages 23536–23557.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Can neural network memorization be localized? In International Conference on Machine Learning, pages 23536–23557

Reference 35

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Observation 0f22006f-a7a4-4463-b145-45de7dda21d2 · outbound

This paper cites The geometry of truth: Emergent linear structure in large language model representations of true/false datasets.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge The geometry of truth: Emergent linear structure in large language model representations of true/false datasets

Reference 36

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Observation 324e89a1-f10f-4586-9dd0-570cf88c1f6d · outbound

This paper cites Locating and editing factual associations in gpt.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Locating and editing factual associations in gpt

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.393825Z

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Observation 5a7c8702-a860-4522-b987-39873f52e014 · outbound

This paper cites What does the knowledge neuron thesis have to do with knowledge? In International Conference on Learning Representations, 2024.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge What does the knowledge neuron thesis have to do with knowledge? In International Conference on Learning Representations, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.377428Z

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This paper cites Interpreting gpt: the logit lens.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Interpreting gpt: the logit lens

Reference 39

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This paper cites Competition of mechanisms: Tracing how language models handle facts and coun- terfactuals.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Competition of mechanisms: Tracing how language models handle facts and coun- terfactuals

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.345678Z

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

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Training language models to follow instructions with human feedback

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.328998Z

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This paper cites Task-specific skill localization in fine-tuned language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Task-specific skill localization in fine-tuned language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.312987Z

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Observation 9870608e-4c31-46cd-87f6-429a3e0f26ef · outbound

This paper cites When do prompting and prefix-tuning work? a theory of capabilities and limitations.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge When do prompting and prefix-tuning work? a theory of capabilities and limitations

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.295203Z

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This paper cites Fine-tuning enhances existing mechanisms: A case study on entity tracking.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Fine-tuning enhances existing mechanisms: A case study on entity tracking

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.277195Z

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Observation e585837d-00c9-4ff6-8a29-67d978c42918 · outbound

This paper cites Neurons in large language models: Dead, n-gram, positional.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Neurons in large language models: Dead, n-gram, positional

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.261278Z

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Observation bb789b86-5448-4e13-9ad5-83fd90a7f718 · outbound

This paper cites Finding skill neurons in pre-trained transformer-based language models.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Finding skill neurons in pre-trained transformer-based language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:02.225231Z

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Observation 1ee714e2-6c90-4c38-9405-4de4a732c500 · outbound

This paper cites Super-NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Super-NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks

Reference 47

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This paper cites Qwen2.5 Technical Report.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Qwen2.5 Technical Report

Reference 48

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This paper cites Knowledge circuits in pretrained transformers.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Knowledge circuits in pretrained transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:01.921457Z

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This paper cites Towards best practices of activation patching in language models: Metrics and methods.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge Towards best practices of activation patching in language models: Metrics and methods

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:01.782256Z

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This paper cites How do large language models handle multilingualism? In Advances in Neural Information Processing Systems, 2024.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge How do large language models handle multilingualism? In Advances in Neural Information Processing Systems, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:01.686449Z

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This paper cites (b) Overlapping Ratio vs.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge (b) Overlapping Ratio vs

Reference 52

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This paper cites He benefited from the world-class education and research facilities at Andrew Jackson University.

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge He benefited from the world-class education and research facilities at Andrew Jackson University

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:01.182317Z

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This paper cites He benefited from the world-class education and research facilities at Andrew Jackson University.

Reference 1982

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:01.362715Z

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Pith citing papers

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Reverse Convolution and Its Applications to Image Restoration cites this paper.

Reverse Convolution and Its Applications to Image Restoration Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge

Reference 6

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Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge

Reference 202

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LMs as Task-Specific Knowledge Bases: An Interpretability Analysis cites this paper.

LMs as Task-Specific Knowledge Bases: An Interpretability Analysis Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge

Reference 71

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