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Paper Citation Record · LEDGER

Test-Time Detoxification without Training or Learning Anything

As of 18 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2602.02498.

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

pith.paper-citation-record.v1
2602.02498 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:40:48.791843Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T04:55:34.529357Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:56:38.824910Z

Reference resolution

56 of 56 outbound references displayed

  • verified exact8
  • verified fuzzy0
  • unresolved45
  • parse uncertain0
  • malformed identifier3
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5fbec80c-406f-45c5-8abe-ebf68d20837b · outbound

This paper cites Llama 3 model card.

Test-Time Detoxification without Training or Learning Anything Llama 3 model card

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:40:42.806327Z digest=sha256:84594a6929d1b65fc01f1289d1c651464f49614482091be17adfc484e566c32d

Observation c28332af-9338-440b-89fb-b20c1817cd09 · outbound

This paper cites Director: Generator-classifiers for supervised language modeling.

Test-Time Detoxification without Training or Learning Anything Director: Generator-classifiers for supervised language modeling

Reference 2

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source=pdf_text observed=2026-08-03T10:40:42.906428Z digest=sha256:65a94f7d8717459b77314c230382d6185573f0673a8cb77b9dc7d69631565122

Observation eeb2f311-d67a-4273-a35a-c0e940209cf1 · outbound

This paper cites Systematic rectification of language models via dead-end analysis.

Test-Time Detoxification without Training or Learning Anything Systematic rectification of language models via dead-end analysis

Reference 4

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source=pdf_text observed=2026-08-03T10:40:43.138049Z digest=sha256:89fbe2d0ab5dd4e60c91a74aabedcc8109c2ff295df766fa55eedf17452d8f60

Observation 82d694e5-1eec-47ff-9527-f509d12f15c6 · outbound

This paper cites Enhancing zeroth-order fine- tuning for language models with low-rank structures.

Test-Time Detoxification without Training or Learning Anything Enhancing zeroth-order fine- tuning for language models with low-rank structures

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:43.238194Z digest=sha256:6236ccf8637c357d30c87a062a7cfb15fd8dd262daa7608f28ef01b15dd639c4

Observation f6e96ec4-ced7-4fe6-971a-43c9d65c1c71 · outbound

This paper cites Plug and play language models: A simple approach to controlled text generation.

Test-Time Detoxification without Training or Learning Anything Plug and play language models: A simple approach to controlled text generation

Reference 7

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source=pdf_text observed=2026-08-03T10:40:43.498895Z digest=sha256:bcf33598282d59df5f066db5098158df7eca1acfc6c25ab7c6cf1e08c204626f

Observation cd67719c-dd48-46c0-9c99-8017865f3d2b · outbound

This paper cites Reward-augmented decoding: Efficient controlled text generation with a unidirectional reward model.

Test-Time Detoxification without Training or Learning Anything Reward-augmented decoding: Efficient controlled text generation with a unidirectional reward model

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:43.632600Z digest=sha256:43fe955dc8c30c89fb082a6f129ca9161861daed04d61bc32da25090a12b9e40

Observation 502b7096-03f6-4cd4-a7d2-45c6438fa9b8 · outbound

This paper cites Bold: Dataset and metrics for measuring biases in open-ended language generation.

Test-Time Detoxification without Training or Learning Anything Bold: Dataset and metrics for measuring biases in open-ended language generation

Reference 9

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:43.705903Z digest=sha256:57646e7c249a4acc2ebde4d90dc03055df5ab0a4f69649edf532419bf7654621

Observation 38cc1a41-5dae-4ff5-946a-059a6481c78e · outbound

This paper cites DSCD: Large lan- guage model detoxification with self-constrained decoding.

Test-Time Detoxification without Training or Learning Anything DSCD: Large lan- guage model detoxification with self-constrained decoding

Reference 10

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source=pdf_text observed=2026-08-03T10:40:43.810434Z digest=sha256:e2151b791287e7bbda106591d969ffc513f2dfac2127c53149b736a3fa2a380f

Observation d7a17d23-64c3-49a0-a178-c7900e578e85 · outbound

This paper cites Variance- reduced zeroth-order methods for fine-tuning language models.

Test-Time Detoxification without Training or Learning Anything Variance- reduced zeroth-order methods for fine-tuning language models

Reference 11

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source=pdf_text observed=2026-08-03T10:40:43.896219Z digest=sha256:3dd046e308546e2e634f08465af1e9d5befdb0b4937aac241f4cd1eacd9cdcd1

Observation 5d51cb23-83d6-4276-9097-909f18453f2b · outbound

This paper cites an unresolved cited work.

Test-Time Detoxification without Training or Learning Anything Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-03T10:40:43.943014Z digest=sha256:e10e0da379c5c630cc2099d40596047c70a321e5aad5c2288dcfb0b8a8cde00c

Observation 157e485f-4594-47b7-b2d2-a0ad8553583e · outbound

This paper cites an unresolved cited work.

Test-Time Detoxification without Training or Learning Anything Unresolved cited work

Reference 13

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source=pdf_text observed=2026-08-03T10:40:44.004827Z digest=sha256:ccb5f0a4cb4708ab6d7b8f6a1163a13b463ba907508513eb7fa73c5434dcc2b2

Observation 579e2b8e-6328-4685-a7f4-fb1084a74cba · outbound

This paper cites Word embeddings are steers for language models.

Test-Time Detoxification without Training or Learning Anything Word embeddings are steers for language models

Reference 14

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source=pdf_text observed=2026-08-03T10:40:44.121222Z digest=sha256:5a55edb0f5c94989c1cab3b421bc62b1d90ede1a6eeb61c91f8879ff46ea609b

Observation ae82e7b5-6a7e-458a-af24-06451250ab3b · outbound

This paper cites Detoxify.

Test-Time Detoxification without Training or Learning Anything Detoxify

Reference 15

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source=pdf_text observed=2026-08-03T10:40:44.209549Z digest=sha256:55a0c7db016cddcb4d08e5c42f593faca664b43e9907635e2a189765057ac0d3

Observation 2c83bf68-9162-4578-a80f-6f67eecf91f2 · outbound

This paper cites Model-free learning of two-stage beamformers for passive irs-aided network design.IEEE Transactions on Signal Processing, 72:652–669,.

Test-Time Detoxification without Training or Learning Anything Model-free learning of two-stage beamformers for passive irs-aided network design.IEEE Transactions on Signal Processing, 72:652–669,

Reference 16

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source=pdf_text observed=2026-08-03T10:40:44.273656Z digest=sha256:afd11f9cf372fc4d30b6c54bfa61dd72b34deb6d409656a02711ae9573dfac73

Observation 084e3342-62b1-4270-8b5f-d95c109b1374 · outbound

This paper cites Localized zeroth-order prompt optimization.

Test-Time Detoxification without Training or Learning Anything Localized zeroth-order prompt optimization

Reference 17

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source=pdf_text observed=2026-08-03T10:40:44.504047Z digest=sha256:3c46e8420c1d4ec4b9ae9c68b20d3e3e23e36281ab60272925fc8200b86bcf6f

Observation f0729dc6-e052-41f2-9071-10a9d4987397 · outbound

This paper cites DAPI: Domain adaptive toxicity probe vector intervention, for fine-grained detoxification.

Test-Time Detoxification without Training or Learning Anything DAPI: Domain adaptive toxicity probe vector intervention, for fine-grained detoxification

Reference 18

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doi, observed 2026-08-03T10:43:24.692646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:44.629329Z digest=sha256:047562a13af339b85b70867155cee3ce61156d83e84d5b7faa958541a4c002a3

Observation 7a283070-9e23-43dd-8b09-b98104127966 · outbound

This paper cites Perspective API.

Test-Time Detoxification without Training or Learning Anything Perspective API

Reference 19

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source=pdf_text observed=2026-08-03T10:40:44.720686Z digest=sha256:731af16634c9104f750f9f772e1c4e3a468378c2f7f79f89ae8d6d218c1c4054

Observation 64ae9e1a-f551-42df-af01-c3b013650296 · outbound

This paper cites Critic- guided decoding for controlled text generation.

Test-Time Detoxification without Training or Learning Anything Critic- guided decoding for controlled text generation

Reference 20

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no resolver link, observed 2026-08-03T10:40:44.795718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:40:44.795718Z digest=sha256:5ed1abd45d818cd345dd47331ed2b663f40b2c2cc5846a61c9174785f5363441

Observation 1f5dc989-3cd5-4410-b345-4148f91f3df4 · outbound

This paper cites Large language models can become strong self- detoxifiers.

Test-Time Detoxification without Training or Learning Anything Large language models can become strong self- detoxifiers

Reference 21

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source=pdf_text observed=2026-08-03T10:40:44.873491Z digest=sha256:3e86a10daee67012e7425bc354bd289eb0c4da8218b39f5420bf6b3e612d83b8

Observation 955af843-20b7-4da8-be3a-be83c641809f · outbound

This paper cites Unveiling safety vulnerabilities of large language models.

Test-Time Detoxification without Training or Learning Anything Unveiling safety vulnerabilities of large language models

Reference 22

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:44.964749Z digest=sha256:5c1cb0195d28deacca8ba0dfda19dc5129344ceaf004105777466696eb961c1d

Observation 216f01e5-d833-448e-8418-0bbfaa8a4875 · outbound

This paper cites GeDi: Generative discriminator guided sequence generation.

Test-Time Detoxification without Training or Learning Anything GeDi: Generative discriminator guided sequence generation

Reference 23

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no resolver link, observed 2026-08-03T10:40:45.034797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:40:45.034797Z digest=sha256:924147978ec993287852afc7a9911f7ed93c57e36596e54ec40529738d8db1a0

Observation 605f244e-891a-47da-90f4-c4ce37916eee · outbound

This paper cites Language detoxification with attribute- discriminative latent space.

Test-Time Detoxification without Training or Learning Anything Language detoxification with attribute- discriminative latent space

Reference 24

Resolution
verified exact
doi, observed 2026-08-03T10:43:24.522898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:45.164770Z digest=sha256:fb3c10322758a019f7f99f56b85e05f58ce38eca2d992039e48b53009f81eb4e

Observation b64555ad-df0f-49e1-b4c4-41dd3838165e · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Test-Time Detoxification without Training or Learning Anything Gonzalez, Hao Zhang, and Ion Stoica

Reference 25

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source=pdf_text observed=2026-08-03T10:40:45.281879Z digest=sha256:d3fab79669742eedb52ffbc74d7c82fac2682f3b87b193599daf500f8411ad23

Observation 03923ffc-0057-44f5-818f-6a76c4982658 · outbound

This paper cites Self-detoxifying language models via toxification reversal.

Test-Time Detoxification without Training or Learning Anything Self-detoxifying language models via toxification reversal

Reference 26

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verified exact
doi, observed 2026-08-03T10:43:24.384596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:45.374977Z digest=sha256:a7195726305f4e9ea53222325b5c03f9cf35b77ee8e870a0b996760e1b7372ea

Observation 0b6fe4ba-a631-48d5-bb9b-a5a7da976b39 · outbound

This paper cites Destein: Navigating detoxification of language models via universal steering pairs and head-wise activation fusion.

Test-Time Detoxification without Training or Learning Anything Destein: Navigating detoxification of language models via universal steering pairs and head-wise activation fusion

Reference 27

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source=pdf_text observed=2026-08-03T10:40:45.485812Z digest=sha256:5857c38611977323d09157a47e6edbbc788a301b8e01eab8c27796059921255a

Observation f8f252cd-af32-4401-a721-52cd7ffecaaf · outbound

This paper cites Smith, and Yejin Choi.

Test-Time Detoxification without Training or Learning Anything Smith, and Yejin Choi

Reference 29

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source=pdf_text observed=2026-08-03T10:40:45.652959Z digest=sha256:cfde4939763010945bedbeebca94f35c4e7c030918e9f1961d6bbb1d8a17b67d

Observation bd5990ec-16a9-4d5c-a7ae-baa7eb3ebede · outbound

This paper cites Sparse meZO: Less parameters for better performance in zeroth-order LLM fine-tuning.

Test-Time Detoxification without Training or Learning Anything Sparse meZO: Less parameters for better performance in zeroth-order LLM fine-tuning

Reference 30

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source=pdf_text observed=2026-08-03T10:40:45.693911Z digest=sha256:e97a24edf628c0d5b5e4c9a76f5fc322da483168fdbea018576be1c578a211b4

Observation bb77aae4-d392-4731-84e4-66f8aacb8cb4 · outbound

This paper cites Unidetox: Universal detoxification of large language models via dataset distillation.

Test-Time Detoxification without Training or Learning Anything Unidetox: Universal detoxification of large language models via dataset distillation

Reference 31

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source=pdf_text observed=2026-08-03T10:40:45.743033Z digest=sha256:46a5af14fcf0190a52935f9ac3eacc66df7c2386461d7a691a1ddcb2c99a5207

Observation f767a2fb-0b8d-4c35-a484-3ae8ee2cd770 · outbound

This paper cites Quark: controllable text generation with reinforced [un]learning.

Test-Time Detoxification without Training or Learning Anything Quark: controllable text generation with reinforced [un]learning

Reference 32

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source=pdf_text observed=2026-08-03T10:40:45.851721Z digest=sha256:27171695d4000453ec2802b2fed2773ebcd8c4489947ff9eb8a46b0dd40d72e5

Observation b19ddedc-2011-4def-a7ff-80975d070efa · outbound

This paper cites Lee, Danqi Chen, and Sanjeev Arora.

Test-Time Detoxification without Training or Learning Anything Lee, Danqi Chen, and Sanjeev Arora

Reference 33

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:40:45.887597Z digest=sha256:64b3e608d5b295e1b1e4e9f606fd96843a565a77ba28dd13e2cf0c95af7bdb67

Observation 18635821-dc52-44cf-91a3-a9e391d55e05 · outbound

This paper cites Attribute controlled fine-tuning for large language models: A case study on detoxification.

Test-Time Detoxification without Training or Learning Anything Attribute controlled fine-tuning for large language models: A case study on detoxification

Reference 34

Resolution
verified exact
doi, observed 2026-08-03T10:43:24.244761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:45.964587Z digest=sha256:71232a2b2112df244c8cb584bacc9af3a06d4ec204dbd49d6f0f244701eabb58

Observation d0402cb6-c075-4fd6-b094-65439c0de751 · outbound

This paper cites Random gradient-free minimization of convex functions.

Test-Time Detoxification without Training or Learning Anything Random gradient-free minimization of convex functions

Reference 35

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source=pdf_text observed=2026-08-03T10:40:46.020125Z digest=sha256:ab852c24ca4f76e8622c8a5ad3d3588b8369e0fc715bc01d7499f75cbc40aec5

Observation 1ddaa856-8604-4f4a-9c8f-448347729a39 · outbound

This paper cites Parameter-efficient detoxification with contrastive decoding.

Test-Time Detoxification without Training or Learning Anything Parameter-efficient detoxification with contrastive decoding

Reference 36

Resolution
verified exact
doi, observed 2026-08-03T10:43:24.076472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:46.058184Z digest=sha256:23ffaa9bd9da8994fb90db9702058592ca20e7ba68927801ad2076896853c7d8

Observation f91f805c-fd2f-4e6e-968d-695a278c72ad · outbound

This paper cites Best practices for prompt engineering with the openai api.

Test-Time Detoxification without Training or Learning Anything Best practices for prompt engineering with the openai api

Reference 37

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source=pdf_text observed=2026-08-03T10:40:46.191848Z digest=sha256:405117bc3b2c2397bc95d698824f3a2d5c3dc551e97caf345a548559f507691e

Observation 8387a427-3026-4492-953a-d608a990e76b · outbound

This paper cites Training language models to follow instructions with human feed- back.

Test-Time Detoxification without Training or Learning Anything Training language models to follow instructions with human feed- back

Reference 38

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:46.307849Z digest=sha256:cb84f9574a0bfd88c553ea259b853eafabf35c56e1e8c4bbaceae62968baa52f

Observation 005b707d-c3a5-4a33-a28c-d496456cc20a · outbound

This paper cites Data-driven learning of two-stage beamformers in passive irs-assisted systems with inexact oracles.IEEE Access, 13:154984–155002, 2025.

Test-Time Detoxification without Training or Learning Anything Data-driven learning of two-stage beamformers in passive irs-assisted systems with inexact oracles.IEEE Access, 13:154984–155002, 2025

Reference 39

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source=pdf_text observed=2026-08-03T10:40:46.421898Z digest=sha256:0d43d1cba3c453149dc413e81b73f467f1fbcbcbe724d000f97c2cc78afc23df

Observation 611a6e23-6b04-4b48-bae3-f7ad91613cc7 · outbound

This paper cites On the challenges of using black-box APIs for toxicity evaluation in research.

Test-Time Detoxification without Training or Learning Anything On the challenges of using black-box APIs for toxicity evaluation in research

Reference 40

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source=pdf_text observed=2026-08-03T10:40:46.579704Z digest=sha256:d62bc8ac2bf07ae79a598d5048d207845164346596dae0d110b1f553c042419d

Observation 40f6cee7-372e-4440-9cea-f43ca655ad48 · outbound

This paper cites Language models are unsupervised multitask learners.

Test-Time Detoxification without Training or Learning Anything Language models are unsupervised multitask learners

Reference 41

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source=pdf_text observed=2026-08-03T10:40:46.741625Z digest=sha256:b1a374e25486e5fa54acae402ab21573598b308a0bdc779affe35026bd11a9d6

Observation c12e80fd-9820-4537-b43a-468bc9c0e001 · outbound

This paper cites Compatible Gradient Approximations for Actor-Critic Algorithms.

Test-Time Detoxification without Training or Learning Anything Compatible Gradient Approximations for Actor-Critic Algorithms

Reference 42

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source=pdf_text observed=2026-08-03T10:40:46.798099Z digest=sha256:31a53ac33331e5c5317162ff9906d7fb819a064a507545da5a82d9053fdc7a8b

Observation 9b4258fc-5a6a-4a57-8ae7-aee8bceed33b · outbound

This paper cites Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp.Transactions of the Association for Computational Linguistics, 9: 1408–1424, 12 2021.

Test-Time Detoxification without Training or Learning Anything Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp.Transactions of the Association for Computational Linguistics, 9: 1408–1424, 12 2021

Reference 43

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source=pdf_text observed=2026-08-03T10:40:46.850563Z digest=sha256:2777370aa9edf42d52501790be69d0916daac7fd4e987d738f063ebc2673084a

Observation d7a9f0f6-951a-4c6c-a1f9-cf09c97b08c6 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Test-Time Detoxification without Training or Learning Anything Gemma 2: Improving Open Language Models at a Practical Size

Reference 44

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source=pdf_text observed=2026-08-03T10:40:46.882802Z digest=sha256:9b66317005ca836a7feb20372c2e7d6152f5150c8b2968ae52feda151c36ce0d

Observation 7db505d2-cf81-445a-96a0-51a605765a09 · outbound

This paper cites Supporting Human Raters with the Detection of Harmful Content Using Large Language Models.

Test-Time Detoxification without Training or Learning Anything Supporting Human Raters with the Detection of Harmful Content Using Large Language Models

Reference 45

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source=pdf_text observed=2026-08-03T10:40:47.108176Z digest=sha256:ea0d3cc8a92e53a4879296e36101b805172ce987c73532af0124e36e1ca1b293

Observation d0fda032-79be-4c75-9c97-1bdff3970190 · outbound

This paper cites Attention is all you need.

Test-Time Detoxification without Training or Learning Anything Attention is all you need

Reference 46

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source=pdf_text observed=2026-08-03T10:40:47.258215Z digest=sha256:25344318397f74197dfd5c0755b8e1e34aa63e4bfe503a449ff8aa89856efbbf

Observation d6608bb6-3a5a-4bf5-8d19-502254983ebc · outbound

This paper cites Exploring the limits of domain-adaptive training for detoxifying large-scale language models.

Test-Time Detoxification without Training or Learning Anything Exploring the limits of domain-adaptive training for detoxifying large-scale language models

Reference 47

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source=pdf_text observed=2026-08-03T10:40:47.413277Z digest=sha256:a55b997c95b8959330f6672307d45242c59cb735fe5f815e51c87a14c8669d6f

Observation 068f988c-52e5-4efc-9fdc-96c9f8aefab7 · outbound

This paper cites Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report.

Test-Time Detoxification without Training or Learning Anything Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report

Reference 48

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source=pdf_text observed=2026-08-03T10:40:47.652225Z digest=sha256:9a4d8b7b10d6d046e67ce42e660e6287719c013d20155d89bafa37f3326e20cf

Observation 5a1f4783-4ead-4eb8-9fa3-2ddf13ed13a2 · outbound

This paper cites Challenges in detoxifying language models.

Test-Time Detoxification without Training or Learning Anything Challenges in detoxifying language models

Reference 49

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verified exact
doi, observed 2026-08-03T10:43:23.959123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:47.814230Z digest=sha256:56d8c805f0ad23255af2739c11f074ceb1c7ff42279ecdb66622d8f51b0ca7fa

Observation ea4d298d-36ed-452f-a225-8b5ed21fe92d · outbound

This paper cites an unresolved cited work.

Test-Time Detoxification without Training or Learning Anything Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-03T10:40:47.969024Z digest=sha256:afac20ef101a700df0a24575433b82962d719bf5b189128aceb7f17fb847bd19

Observation b30a5345-9c90-4959-a990-ed83e702c121 · outbound

This paper cites Recipes for Safety in Open-domain Chatbots.

Test-Time Detoxification without Training or Learning Anything Recipes for Safety in Open-domain Chatbots

Reference 51

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no resolver link, observed 2026-08-03T10:40:48.133852Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:48.133852Z digest=sha256:174f7dd11b8aaa374329a070dcabbe6d9160ba7a2943af6e4b72f1144b92b488

Observation 06bb1a45-8d5d-4ac7-85b0-c841b522d2e5 · outbound

This paper cites SafeDecoding: Defending against jailbreak attacks via safety-aware decoding.

Test-Time Detoxification without Training or Learning Anything SafeDecoding: Defending against jailbreak attacks via safety-aware decoding

Reference 52

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no resolver link, observed 2026-08-03T10:40:48.239022Z

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source=pdf_text observed=2026-08-03T10:40:48.239022Z digest=sha256:bb2546c2d6bab970b4272524a0aae5918ee146a9181151577345515e97450f83

Observation f86dc234-3023-4839-8f86-4b978e448fd0 · outbound

This paper cites Qwen3 Technical Report.

Test-Time Detoxification without Training or Learning Anything Qwen3 Technical Report

Reference 53

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no resolver link, observed 2026-08-03T10:40:48.406588Z

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source=pdf_text observed=2026-08-03T10:40:48.406588Z digest=sha256:38870f5a4123172d2672f436ecf0f3b91bfc27057a1798f2727af1214bd569e4

Observation 836aad66-9385-4267-ab9a-c0498fe189bf · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.303.

Test-Time Detoxification without Training or Learning Anything doi: 10.18653/v1/2024.acl-long.303

Reference 54

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no resolver link, observed 2026-08-03T10:40:48.360785Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:48.360785Z digest=sha256:2c1d4ab130994da938e9facd2e243828644f1f70a4402d67def45eb07c7f0105

Observation 2f576f16-a0d8-4fca-8992-9dfa7bcc1f9d · outbound

This paper cites Unlocking black-box prompt tuning efficiency via zeroth-order optimization.

Test-Time Detoxification without Training or Learning Anything Unlocking black-box prompt tuning efficiency via zeroth-order optimization

Reference 55

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malformed identifier
no resolver link, observed 2026-08-03T10:40:48.581549Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:40:48.581549Z digest=sha256:1dc52ff24bd6908b49668c0c3c47ce58d7a7c6f322a096cd85a4c8ad30eb008d

Observation 47e91581-2346-4096-90cc-4fe25c9bcce4 · outbound

This paper cites FUDGE: Controlled text generation with future discriminators.

Test-Time Detoxification without Training or Learning Anything FUDGE: Controlled text generation with future discriminators

Reference 56

Resolution
verified exact
doi, observed 2026-08-03T10:43:23.852656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:48.508273Z digest=sha256:032dad5d20523d1b331ca8bee5b73df5ae2ced3e081d9bb52694f3de0b9aa5aa

Observation 3e9072a0-91cb-4d51-8f1b-19ca041561a0 · outbound

This paper cites challenging.

Test-Time Detoxification without Training or Learning Anything challenging

Reference 57

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source=pdf_text observed=2026-08-03T10:40:48.791843Z digest=sha256:d597bd5625959509c26520db962473de7ed5738ac543cf9904c3f25ed2a9936a

Observation 34348249-bd37-40b0-8a86-671f35d610f6 · outbound

This paper cites Detoxifying large language models via the diversity of toxic samples.

Test-Time Detoxification without Training or Learning Anything Detoxifying large language models via the diversity of toxic samples

Reference 58

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verified exact
doi, observed 2026-08-03T10:43:23.746647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-03T10:40:48.682146Z digest=sha256:d8429f0d62d4e7ca8bd7f4d3ab5a80ecaa25a8b1124f3f068984f35dab66b5ba

Observation 7ef13665-fc61-4fb2-9cff-e194a52b1c7f · outbound

This paper cites an unresolved cited work.

Test-Time Detoxification without Training or Learning Anything Unresolved cited work

Reference 2024

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source=pdf_text observed=2026-08-03T10:40:44.411215Z digest=sha256:723c775b80c9b17bf3740183836b34a068d01737494caea006839cb042489da4

Pith citing papers

Observation 4ef9a461-ef0a-4c98-845a-2a15d2b6f4fe · inbound

Self-Improving In-Context Learning cites this paper.

Self-Improving In-Context Learning Test-Time Detoxification without Training or Learning Anything

Reference 39

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arxiv_id, observed 2026-06-30T02:16:14.044451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-25T04:55:34.529357Z digest=sha256:4595d708262fe9d7cd68f89b27db49fcee09f6971f9f03f97d3b2a10d68d7e9a