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Paper Citation Record · LEDGER

Watermarking LLM-Generated Datasets in Downstream Tasks

As of 16 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2506.13494.

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

pith.paper-citation-record.v1
2506.13494 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:05:13.806297Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

58 of 58 outbound references displayed

  • verified exact1
  • verified fuzzy33
  • unresolved22
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f21f770a-5e0a-4e3d-9752-2142a82234c7 · outbound

This paper cites an unresolved cited work.

Watermarking LLM-Generated Datasets in Downstream Tasks Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-15T20:05:14.501622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 294c8eda-43c8-45bf-b16e-4d6a01436ba1 · outbound

This paper cites an unresolved cited work.

Watermarking LLM-Generated Datasets in Downstream Tasks Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.586157Z digest=sha256:59746072412770bb637440eac72c714389a07d9ed891be4aa19088f047d0d451

Observation 5e3f115b-32c4-45f9-a4bf-451414c47d55 · outbound

This paper cites an unresolved cited work.

Watermarking LLM-Generated Datasets in Downstream Tasks Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.590572Z digest=sha256:053327fc1771db6a4689c471b4ea215f1d4f6c591f0af79a9e8cd7acf3536cbb

Observation c19b11aa-276c-40b6-99ef-578d70ed0d40 · outbound

This paper cites Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring.

Watermarking LLM-Generated Datasets in Downstream Tasks Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.471644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.594463Z digest=sha256:4429b85b4f1c599b0d49a91dde18218b813468bc06265ac67662abbb02ce3496

Observation 6df6c065-0643-42fb-bdde-86fa61f206a5 · outbound

This paper cites Benchmarking Large Language Models in Retrieval- Augmented Generation.

Watermarking LLM-Generated Datasets in Downstream Tasks Benchmarking Large Language Models in Retrieval- Augmented Generation

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.461336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.598889Z digest=sha256:d612025dcd5ec49093fd8b7a35686a1105810b68d68c58869f982f49d29c5568

Observation fc60c94e-7384-4928-b6cd-49388e42d49a · outbound

This paper cites BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements.

Watermarking LLM-Generated Datasets in Downstream Tasks BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.451137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.602594Z digest=sha256:949becd79ec2d9ff03b7b5de9624f46504383d922b4de456af9d16380288404d

Observation 774ab2c9-0374-48a4-9c2c-2baff3bc92d2 · outbound

This paper cites REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data.

Watermarking LLM-Generated Datasets in Downstream Tasks REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.440403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.606789Z digest=sha256:464c709e874efd24171019a1b6746e35f80e5058ac985aaab097167d131e4938

Observation 6f0ff7fa-4da7-4d74-b59b-41dfafd005db · outbound

This paper cites DialogSum: A Real-Life Scenario Dialogue Summarization Dataset.

Watermarking LLM-Generated Datasets in Downstream Tasks DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.610432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.610432Z digest=sha256:9ca47782e868f7f443f23a28b13a97e736c0a901908830e547510af1bb3a8820

Observation 88fd7c3b-c086-431f-b272-11eaaff58099 · outbound

This paper cites Increasing Diversity While Maintaining Ac- curacy: Text Data Generation with Large Language Models and Human Interventions.

Watermarking LLM-Generated Datasets in Downstream Tasks Increasing Diversity While Maintaining Ac- curacy: Text Data Generation with Large Language Models and Human Interventions

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.429454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.614411Z digest=sha256:e663cd3ba09bee83ed6359abd4ecc7e16d1015f999d7c0de3235d605388d09ae

Observation af7e8229-016b-4afa-ac9d-c8e2815257c4 · outbound

This paper cites SSL- Guard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders.

Watermarking LLM-Generated Datasets in Downstream Tasks SSL- Guard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.419129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.618625Z digest=sha256:238bec6d4dfce7091baed6a118ac29034c5253b5197401eb2df88280d085b5a3

Observation 56e007be-c73a-464c-8c80-590b81d401a1 · outbound

This paper cites BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding.

Watermarking LLM-Generated Datasets in Downstream Tasks BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.410000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.622335Z digest=sha256:e6a929f452179f993d27d789af008f2ea6575d18c0b2ee1a4607ec9f438aeed5

Observation ec1dbe64-129e-437f-ab22-bbb99306cbfb · outbound

This paper cites Watermark Removal Scheme Based on Neural Network Model Pruning.

Watermarking LLM-Generated Datasets in Downstream Tasks Watermark Removal Scheme Based on Neural Network Model Pruning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.399935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.625975Z digest=sha256:7ba289470163fc9f890b088e9b7c8a3e31b038cc5ed350670b97d877a4eaf889

Observation a8478f51-98b2-4f98-8bda-39ce046cf39b · outbound

This paper cites Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models.

Watermarking LLM-Generated Datasets in Downstream Tasks Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.388865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.629634Z digest=sha256:6ef1f339df53cc81275362612ec6a35a7aeaad2ab8aacc9b616b275c6b8221f9

Observation ece92ced-ce9c-40cd-93c3-ee64ab2a5edd · outbound

This paper cites Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot.

Watermarking LLM-Generated Datasets in Downstream Tasks Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.377407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.634292Z digest=sha256:cb9e1e70c47207e7bfec5f455d5d2dfb3ddbce7c4fc82cab8f300e7190616ee8

Observation 877653c0-1ad8-4410-bbe0-deecc15ca846 · outbound

This paper cites Watermark Stealing in Large Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks Watermark Stealing in Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.641762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.641762Z digest=sha256:8f05a6c520b2a11a18b0428cf97507f094b3d78d6195bdfd82857deb578a06c5

Observation 3b370c68-d58e-44f9-ba5f-25ae2a105d08 · outbound

This paper cites A Watermark for Large Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks A Watermark for Large Language Models

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.365864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.646119Z digest=sha256:3958519646fa226754407b328daff7346a687b8297bff901eaffb9ba7913ddea

Observation c7d9d5f9-7355-4407-ba33-36326f4ee8e9 · outbound

This paper cites On the Reliability of Watermarks for Large Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks On the Reliability of Watermarks for Large Language Models

Reference 17

Resolution
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no resolver link, observed 2026-08-15T20:05:13.649880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.649880Z digest=sha256:caa1faf71bbfcd75d990233cba2844260a794880a3a37c06f22eaeb674fdedd9

Observation 6f3cb3ad-6ad4-4fd9-966d-496e4b041af5 · outbound

This paper cites Large Lan- guage Models are Zero-Shot Reasoners.

Watermarking LLM-Generated Datasets in Downstream Tasks Large Lan- guage Models are Zero-Shot Reasoners

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.354883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.653752Z digest=sha256:703e9d57458331a64221a46213fb459b4a61e969723cfffff23806266bc61483

Observation 1dd7d24e-7d69-48e8-98c3-b6cf94fc0119 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

Watermarking LLM-Generated Datasets in Downstream Tasks Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 19

Resolution
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no resolver link, observed 2026-08-15T20:05:13.658014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.658014Z digest=sha256:fe58edcd79ed77c47b66ce2bba53a64ecdba5ff173883eb41b63c4881089626b

Observation e2810871-7c1a-4425-bab5-c17a23e03708 · outbound

This paper cites Who Wrote this Code? Watermarking for Code Generation.

Watermarking LLM-Generated Datasets in Downstream Tasks Who Wrote this Code? Watermarking for Code Generation

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.343826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.662335Z digest=sha256:62efb477d0bc15c0cd1015dcc32e1e46d5cce87d2ab24d84139278bf31192f32

Observation 836de15c-2712-4e82-becc-cb8fdf2d9c94 · outbound

This paper cites PLMmark: A Se- cure and Robust Black-Box Watermarking Framework for Pre-trained Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks PLMmark: A Se- cure and Robust Black-Box Watermarking Framework for Pre-trained Language Models

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.334513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.666229Z digest=sha256:507af58174b8bff7a47612f70acbfa9774eaf42aee0214a45f28c1b2e3ea1313

Observation db8176b6-8302-4cad-89b0-934a440747f3 · outbound

This paper cites Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limita- tions.

Watermarking LLM-Generated Datasets in Downstream Tasks Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limita- tions

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.325164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.669940Z digest=sha256:c627ec649bf235629961500736953bb802d8ee1126e3e37bd8f38c35934b46d3

Observation 1cf380d7-8840-47db-87cd-04d15168c19a · outbound

This paper cites Mapping the Increasing Use of LLMs in Scientific Papers.

Watermarking LLM-Generated Datasets in Downstream Tasks Mapping the Increasing Use of LLMs in Scientific Papers

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.673700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.673700Z digest=sha256:9edd6fa2ad3c5db48166e0ef16fd47a2828af1f2b796f79bd47a93988b9a266d

Observation 5709cfbd-2ad9-4e83-bea5-9bbf95ab70ad · outbound

This paper cites A Semantic Invariant Robust Watermark for Large Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks A Semantic Invariant Robust Watermark for Large Language Models

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.314053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.678029Z digest=sha256:d85eb2fbfbfc808fed7d7423aa808630f8010a3deb8e67ebb83673d258fe8c02

Observation d040c723-74a4-4a6a-898d-59b7abaeabcb · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Watermarking LLM-Generated Datasets in Downstream Tasks Improved Baselines with Visual Instruction Tuning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.681598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.681598Z digest=sha256:47a345eaa15adb9f733d4fdd8f82465bf4cb6b34e34cc273ea4d75c89dc2619f

Observation 2b6d8041-2ee6-4f56-8685-68de15c1ea5d · outbound

This paper cites Fine-Pruning: Defending Against Backdooring At- tacks on Deep Neural Networks.

Watermarking LLM-Generated Datasets in Downstream Tasks Fine-Pruning: Defending Against Backdooring At- tacks on Deep Neural Networks

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.302410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.686129Z digest=sha256:ca95b160e010bd7e7500dd4ecf61af0bfd151fb190038277f463f3e52f1c62d9

Observation a72c2e65-4f76-4a6f-98fb-7cae0e049107 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Watermarking LLM-Generated Datasets in Downstream Tasks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.693923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.693923Z digest=sha256:c1616d06c443bf986ee3b8879732af19adccad001fe8cf437394728669a5df28

Observation 3474027f-9fcc-4880-8ed7-fb9e9ab6415f · outbound

This paper cites Robustness Over Time: Understanding Adversarial Examples’ Effective- ness on Longitudinal Versions of Large Language Mod- els.

Watermarking LLM-Generated Datasets in Downstream Tasks Robustness Over Time: Understanding Adversarial Examples’ Effective- ness on Longitudinal Versions of Large Language Mod- els

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.698022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.698022Z digest=sha256:478c0c8055db467ceeacdbc157108ada9727e97b07478f3ecb50481d8ef7649a

Observation 10c4be50-fcb6-4475-9dbd-659d3484340a · outbound

This paper cites Backdoor Attacks Against Dataset Distillation.

Watermarking LLM-Generated Datasets in Downstream Tasks Backdoor Attacks Against Dataset Distillation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.701832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.701832Z digest=sha256:91bb0085f433e0f488f6a5bb88e917021ca9b2160019ecb325d9d898ccc25a45

Observation f50ef0e8-70fc-4dd9-b5f6-e332d0090b9b · outbound

This paper cites Watermarking Diffusion Model.

Watermarking LLM-Generated Datasets in Downstream Tasks Watermarking Diffusion Model

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.705252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.705252Z digest=sha256:e322b0dadb88dbfe7cb1015b72ef040977cb94e125b9bfebec25d747c8dc4b5f

Observation c354a406-f805-408c-a260-2b2546571b17 · outbound

This paper cites SoK: How Robust is Image Classification Deep Neural Network Watermarking? In IEEE Sympo- sium on Security and Privacy (S&P).

Watermarking LLM-Generated Datasets in Downstream Tasks SoK: How Robust is Image Classification Deep Neural Network Watermarking? In IEEE Sympo- sium on Security and Privacy (S&P)

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.280560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.708808Z digest=sha256:4c77a8e84abbed79a688005db0e0c92fd269043e2b3962f23fb2aa7dd8fcce2b

Observation dcc0d5b4-0601-453e-bbcc-9cb077d8fe75 · outbound

This paper cites Maas, Raymond E.

Watermarking LLM-Generated Datasets in Downstream Tasks Maas, Raymond E

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.269857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.711999Z digest=sha256:bbc224fd145047f64a0e807fdf77d129fb4177a57b19e60122d6c7f784b6353b

Observation 0e7d8361-3dd8-4824-921c-9facf154e29f · outbound

This paper cites Adversarial Frontier Stitching for Remote Neural Network Watermarking.

Watermarking LLM-Generated Datasets in Downstream Tasks Adversarial Frontier Stitching for Remote Neural Network Watermarking

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-15T20:05:13.926416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.715549Z digest=sha256:d2705603101b1addfc8de89f6f4a7ea3bad37c7dff89995d4a7d08ee47fb47e4

Observation bfcd9b10-9cf4-498e-a29a-7b7d7eb7bf79 · outbound

This paper cites Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks.

Watermarking LLM-Generated Datasets in Downstream Tasks Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.259070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.719616Z digest=sha256:47814626745b29b5b3f8a8e63677d46e39d09e00f70d76937e83d06cead9a1f1

Observation 90fc52f7-a726-444e-a1e3-a9fcbbe55770 · outbound

This paper cites GPT-4 Technical Report.

Watermarking LLM-Generated Datasets in Downstream Tasks GPT-4 Technical Report

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.723763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.723763Z digest=sha256:b5566b31b4ff43a0dfbcecf38c3ffc15c82b31f3291b991b834f369324742e21

Observation 614a7207-2f59-4f95-ae2a-6d002ea92906 · outbound

This paper cites What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning.

Watermarking LLM-Generated Datasets in Downstream Tasks What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.727499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.727499Z digest=sha256:909661290e95cf0cbd2847ae12689b4e9adbfbf51d6df09c10ec226fd54e9826

Observation f9ad02e8-5cd9-4a23-832e-55db64dde343 · outbound

This paper cites Hidden Trigger Backdoor Attack on NLP Models via Linguistic Style Manipulation.

Watermarking LLM-Generated Datasets in Downstream Tasks Hidden Trigger Backdoor Attack on NLP Models via Linguistic Style Manipulation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.246216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.731645Z digest=sha256:b4e8c6e813822eb4ba214f09d19ac7b8f7bf6e6dd8fc17f373db89a5419ceee6

Observation be0a15fe-e27e-4156-80c2-caba2d995a77 · outbound

This paper cites No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices.

Watermarking LLM-Generated Datasets in Downstream Tasks No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.735365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.735365Z digest=sha256:5ba9fd96d4d8d4342ff08ae811c73e2e4229f607585c255b4a2cf1294e8e6c75

Observation 19d1feb2-7c0b-42cb-a7ae-2ae20534a9a6 · outbound

This paper cites Can Large Language Models Rea- son about Program Invariants? In International Con- ference on Machine Learning (ICML).

Watermarking LLM-Generated Datasets in Downstream Tasks Can Large Language Models Rea- son about Program Invariants? In International Con- ference on Machine Learning (ICML)

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.235516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.739346Z digest=sha256:baf7ff004a0fd37b25c0382d18e7608029fc2220b4a82446b1a6a0b420bce5e1

Observation f5cfd16e-5c8f-40f8-88dc-f78535ddc13a · outbound

This paper cites Are You Copying My 16 Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark.

Watermarking LLM-Generated Datasets in Downstream Tasks Are You Copying My 16 Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.224841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.742709Z digest=sha256:1f395b3300b7161dc7a22613e8e7c318b2ea714b1e9975e866e2b3dbd5339ccd

Observation a5f497f4-ec59-4027-8f15-a0d734377988 · outbound

This paper cites MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Fron- tiers.

Watermarking LLM-Generated Datasets in Downstream Tasks MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Fron- tiers

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.214225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.746443Z digest=sha256:d8fa5fce61c157fb878b75e165e1497829c790274a63ff89626d5d3343dc46aa

Observation b377f731-04e0-4c54-b286-2852a122e48c · outbound

This paper cites an unresolved cited work.

Watermarking LLM-Generated Datasets in Downstream Tasks Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-15T20:05:14.202873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.751009Z digest=sha256:bc84f71b25ef55219602730f81d671afe494e7b9cbb74886dad0f030e671b1ae

Observation d8b16b26-6ce3-487b-955f-d0137e1e9388 · outbound

This paper cites A Robust Semantics-based Watermark for Large Language Model against Paraphrasing.

Watermarking LLM-Generated Datasets in Downstream Tasks A Robust Semantics-based Watermark for Large Language Model against Paraphrasing

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.754604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.754604Z digest=sha256:2bfad3cfb91880b263d808e22aea82e2a202efef8d124e1202d384f5844c7e98

Observation ecf92461-5a14-4a0d-84cf-e9ddfef10b5b · outbound

This paper cites DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models.

Watermarking LLM-Generated Datasets in Downstream Tasks DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.758607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.758607Z digest=sha256:211ef2dd7766b92240d75e3287bf4f4b3e7fa6a8ecb51092b70fe2f664ffae54

Observation 4e038ada-5af4-43ac-a73e-ecc55d8aa876 · outbound

This paper cites Embedding Watermarks into Deep Neural Networks.

Watermarking LLM-Generated Datasets in Downstream Tasks Embedding Watermarks into Deep Neural Networks

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.190816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.762917Z digest=sha256:7a33e86d4d8b15b3ddefbb53e15bc4f343675f75690681a91d04e339edbc4506

Observation 26ae3b52-3151-45fd-922c-db8fb6306290 · outbound

This paper cites Attacks on Digital Watermarks for Deep Neural Networks.

Watermarking LLM-Generated Datasets in Downstream Tasks Attacks on Digital Watermarks for Deep Neural Networks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.180912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.766580Z digest=sha256:70f8496ea3e3b1beb71b7736dde63551bd72e818860923eb81ad4b39bc0a7159

Observation 3cb94ae6-7c65-4d89-9f35-da10ce05c7b4 · outbound

This paper cites Chi, Quoc V.

Watermarking LLM-Generated Datasets in Downstream Tasks Chi, Quoc V

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.169288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.769524Z digest=sha256:839daa18e7f95a3c4649ff39229f1cce530c98e680ecda89a9417a155c15b6dd

Observation b765b2b0-a7d7-48d1-b9bc-540dcbd4e803 · outbound

This paper cites Qwen2 Technical Report.

Watermarking LLM-Generated Datasets in Downstream Tasks Qwen2 Technical Report

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.772794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.772794Z digest=sha256:4535d2cb14cd1dc4f7daf503471a1adffdd46d683ac668d26a718d3dd8069c21

Observation c964d4b0-05eb-47a1-867b-bd2f5b2e28ec · outbound

This paper cites Qwen2.5 Technical Report.

Watermarking LLM-Generated Datasets in Downstream Tasks Qwen2.5 Technical Report

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.776333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.776333Z digest=sha256:8d353a8fd6b2ffe955563ed7e89a66a714ab6cef516d10098a3ac113eb1308ed

Observation 42be5da1-8f5f-4cf2-a655-9585ca2a4fda · outbound

This paper cites Stoecklin, Heqing Huang, and Ian Molloy.

Watermarking LLM-Generated Datasets in Downstream Tasks Stoecklin, Heqing Huang, and Ian Molloy

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.157727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.779911Z digest=sha256:89543bf5300162d5b95de45c5d87d44df5ea959af8cf1a2d64bcfe3cd56697d1

Observation 473fa5af-2c68-4bc6-b097-28e9e56a59f5 · outbound

This paper cites Instruction Backdoor Attacks Against Cus- tomized LLMs.

Watermarking LLM-Generated Datasets in Downstream Tasks Instruction Backdoor Attacks Against Cus- tomized LLMs

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.145588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.783366Z digest=sha256:0d0bd51a211044cb638ae9610a5a4e3e0f5b2b4d1935f2b270f82b057bc5a47e

Observation b95b7335-8f4d-4547-8b2f-a9bcadf61973 · outbound

This paper cites Character-level Convolutional Networks for Text Clas- sification.

Watermarking LLM-Generated Datasets in Downstream Tasks Character-level Convolutional Networks for Text Clas- sification

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.132555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.787090Z digest=sha256:4dec98b08c7e54392f74902be672cc5408da16721479e535d270cdb362b07180

Observation b1acfadc-23f9-4dff-812b-c4c29bd25581 · outbound

This paper cites Provable Robust Watermarking for AI-Generated Text.

Watermarking LLM-Generated Datasets in Downstream Tasks Provable Robust Watermarking for AI-Generated Text

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.122309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.790806Z digest=sha256:43d66fdc55683a0056b0d94f6c6a9141a8864992ea8bf0ab7c91c220f46a09b4

Observation 17b42f32-6dc5-4e3f-9f4b-d62e47324d90 · outbound

This paper cites Attention Distrac- tion: Watermark Removal Through Continual Learn- ing with Selective Forgetting.

Watermarking LLM-Generated Datasets in Downstream Tasks Attention Distrac- tion: Watermark Removal Through Continual Learn- ing with Selective Forgetting

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.110614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.794540Z digest=sha256:9b65ca100b892f5dab2181c993c0c7818f3e14fd88e8329ee0354abd56ac61dc

Observation 309e7638-2de3-440d-bfb6-9128bdd7da02 · outbound

This paper cites Large Language Models are Human-Level Prompt En- gineers.

Watermarking LLM-Generated Datasets in Downstream Tasks Large Language Models are Human-Level Prompt En- gineers

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:05:14.099059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.798814Z digest=sha256:9711dce3852181704e759daab73dc259c5016c2d68de295008a8f461355947d1

Observation 2e3487eb-5544-4941-8cc8-a28470d33ec7 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Watermarking LLM-Generated Datasets in Downstream Tasks MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:13.802572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:05:13.802572Z digest=sha256:da694f0163d0a7aefbc3e512697d92a9be4c7d2c3d86447e1ab8dc3b9504dc6a

Observation d46fb075-40ed-4356-8f5c-66dd6a7fda23 · outbound

This paper cites To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression.

Watermarking LLM-Generated Datasets in Downstream Tasks To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression

Reference 57

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T20:05:14.088526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.806297Z digest=sha256:1f8efccee5aec34e5dd1a6de9beb7d993efca00cfc39292579bddfdf8e30cd71

Observation 13b20512-a735-4a35-9216-660dd4aaf8b1 · outbound

This paper cites an unresolved cited work.

Watermarking LLM-Generated Datasets in Downstream Tasks Unresolved cited work

Reference 294

Resolution
parse uncertain
raw_fallback, observed 2026-08-15T20:05:14.291279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:05:13.690312Z digest=sha256:5555fcd888f980d546aebc3390c1b5f9cbf76da2a0fdfa8b8f5684b5220c2315

Pith citing papers

No inbound Pith citation observations are available.