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Paper Citation Record · LEDGER

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution

As of 21 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2508.21004.

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

pith.paper-citation-record.v1
2508.21004 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:42:31.580341Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

70 of 70 outbound references displayed

  • verified exact4
  • verified fuzzy32
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a0a5d895-d301-4c74-886d-c40632fdf25c · outbound

This paper cites Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge

Reference 1

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source=pdf_text observed=2026-08-05T14:42:31.364363Z digest=sha256:207db78bb6d68780051d269400713e5d5c5ca407e72aa4b7ef80149513465bfa

Observation c5811157-051e-4fe1-9ea9-f33955ee1f64 · outbound

This paper cites How to backdoor federated learning.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution How to backdoor federated learning

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.368634Z digest=sha256:f1db0bdd3f9827bb5b988fd314041f3eab6f4eb41b90d474d3481ddfe91eda6e

Observation d96958ec-2210-461b-9389-96461161e00e · outbound

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

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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source=pdf_text observed=2026-08-05T14:42:31.372096Z digest=sha256:ffb2da73328da4ce73e5d1954dc11b36e017c4c66636b4ca0167c0910d54932c

Observation dad8bf1a-ed63-4728-b820-5001d9322427 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Evaluating Large Language Models Trained on Code

Reference 4

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source=pdf_text observed=2026-08-05T14:42:31.376514Z digest=sha256:988a3bb8284efc1d9ebfe111d5c6d34d70ee7465c2aad9e13b4cfdf72c4dfadb

Observation bfe74251-2c9b-4027-b210-a1a5df4ce454 · outbound

This paper cites Backdoor attacks and coun- termeasures in natural language processing models: A comprehensive security review.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Backdoor attacks and coun- termeasures in natural language processing models: A comprehensive security review

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.380182Z digest=sha256:cc5675edb673ada9e9827c519081e89b9a6d0189c720413ef8fc1e631c57673d

Observation 3bf82dd6-96ae-4695-b69d-5ff9894baa90 · outbound

This paper cites Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

Reference 6

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source=pdf_text observed=2026-08-05T14:42:31.383618Z digest=sha256:56f61a2d90742334eb62ca789346b4fde5ba6ae08856cc20d50bbf288dcfead5

Observation 8aa7dde6-4bfb-4860-bd96-e88ddc227d69 · outbound

This paper cites Arcee's MergeKit: A Toolkit for Merging Large Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Arcee's MergeKit: A Toolkit for Merging Large Language Models

Reference 7

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source=pdf_text observed=2026-08-05T14:42:31.387903Z digest=sha256:e4042c818d820bc79736c9b4cf847f24c0c608fd187a47a38f77267ec517255e

Observation ba410568-ab14-4ac7-82c5-2cfbc8b0f022 · outbound

This paper cites Defense-resistant backdoor attacks against deep neural networks in outsourced cloud environment.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Defense-resistant backdoor attacks against deep neural networks in outsourced cloud environment

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.391436Z digest=sha256:b5e386032c3ea45ed8f4588fc3ab99a01c9a9eadee13ea1d02453a076c5dafd8

Observation 760e78b0-82b2-4f4f-aa8a-187b83f41cd7 · outbound

This paper cites Redeem myself: Purifying backdoors in deep learning models using self attention distillation.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Redeem myself: Purifying backdoors in deep learning models using self attention distillation

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.394531Z digest=sha256:348496f8c62e046f4108cdbc06883e1181e17fb25c0bf2597f068902812a1f73

Observation 98c8c21b-383a-45e0-8c63-4396c1c25704 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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source=pdf_text observed=2026-08-05T14:42:31.397826Z digest=sha256:bbcdc54c1e7472038f527542e497eaef380cf7301bfd9a8c996e220954c47f56

Observation 86752fb4-e2b1-4eef-96c7-5cacb05e0185 · outbound

This paper cites Exploring Backdoor Vulnerabilities of Chat Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Exploring Backdoor Vulnerabilities of Chat Models

Reference 11

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.401901Z digest=sha256:290365e6eced11e892291366e3a6e68ae35e05369ec9d8c07990045332c9de32

Observation b57f3095-a490-4e6a-9aa9-d7567f752f36 · outbound

This paper cites Handcrafted Backdoors in Deep Neural Networks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Handcrafted Backdoors in Deep Neural Networks

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.405190Z digest=sha256:1c31393a69f40438b23ba8af1720a32a7a95bfed16678014e16f70007e45a676

Observation ed51ec82-d691-4b83-a3bc-9eacd219e75b · outbound

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

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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source=pdf_text observed=2026-08-05T14:42:31.408645Z digest=sha256:e34beb0eb30e61e46f9280bea508d9f06165f0415a8a91c5e642b544f0b5ea4f

Observation a825e185-76da-4ed5-a184-b976771b1aae · outbound

This paper cites Composite Backdoor Attacks Against Large Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Composite Backdoor Attacks Against Large Language Models

Reference 14

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source=pdf_text observed=2026-08-05T14:42:31.411783Z digest=sha256:f84c52691e02c92aec909b200998fe57d11949383665a5e37a3ccbc63f2a56d2

Observation 98a517cc-c1bb-43c6-9687-638b11835ac7 · outbound

This paper cites Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Reference 15

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source=pdf_text observed=2026-08-05T14:42:31.415328Z digest=sha256:fbf5bb895acee5dc1fcf7bed5e234d5525b0deb25a5a486b7b42040a704190e2

Observation 8c267720-eef9-4c24-ad4e-bd60def996a6 · outbound

This paper cites Editing Models with Task Arithmetic.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Editing Models with Task Arithmetic

Reference 16

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source=pdf_text observed=2026-08-05T14:42:31.418317Z digest=sha256:2679e12363334d729cca68d5f62c52c735180f07e11aa6349ffdb51e83e14bde

Observation 79033d8d-3c5a-4c77-9892-0f9d7bbb0aaa · outbound

This paper cites Chatgpt for good? On opportunities and challenges of large language models for education.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Chatgpt for good? On opportunities and challenges of large language models for education

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.421550Z digest=sha256:17a500783eb5e06177e9d8d5317727a72d21b3a2aea1d50796a5ef1449f60b5e

Observation dcd67056-6a5a-48ab-af8d-b7dc016a58ad · outbound

This paper cites Fast inference from transformers via speculative decoding.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fast inference from transformers via speculative decoding

Reference 18

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source=pdf_text observed=2026-08-05T14:42:31.424498Z digest=sha256:cb5845d584bc7fae215f2cc5397b50503401210c41bca406f8836ff133c8e5f8

Observation 16f98be6-3927-4e38-86e2-ce47f1e565ff · outbound

This paper cites Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning

Reference 19

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source=pdf_text observed=2026-08-05T14:42:31.427248Z digest=sha256:702a923f5045da486dedf5b8f9726dd860f8da11d5da8bdf3be6268cb763f348

Observation 5bd34cf6-b537-42ca-9290-cb484ca80d23 · outbound

This paper cites Chain-of-scrutiny: Detecting backdoor 15 attacks for large language models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Chain-of-scrutiny: Detecting backdoor 15 attacks for large language models

Reference 20

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source=pdf_text observed=2026-08-05T14:42:31.430104Z digest=sha256:db138d861a65281082c0d26c382b1238db1784f6c33a406585967543430b68a3

Observation 96acead6-99a4-4235-83b9-d97f02eb7c00 · outbound

This paper cites BadEdit: Backdooring large language models by model editing.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution BadEdit: Backdooring large language models by model editing

Reference 21

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source=pdf_text observed=2026-08-05T14:42:31.432721Z digest=sha256:ad742b5b75669472459915d992d99b4fa9648fbdf47ef5b50f0912707919f18e

Observation b5012e99-a571-485f-9ab2-bbf7774931db · outbound

This paper cites Multi-target Backdoor Attacks for Code Pre-trained Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Multi-target Backdoor Attacks for Code Pre-trained Models

Reference 22

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source=pdf_text observed=2026-08-05T14:42:31.435614Z digest=sha256:3140e1e2a082c33ec65c5d6d83c4375c2db96bdb41f7158f095a6aeea02534df

Observation b00e6b43-a136-412c-99a6-99c6a66fdc71 · outbound

This paper cites Neural attention distillation: Erasing backdoor triggers from deep neural networks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Neural attention distillation: Erasing backdoor triggers from deep neural networks

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.438598Z digest=sha256:cfc2c895b21a7b26fbf5844b7fc8ce582517bf9faaa5389ea60bfe6305f7471c

Observation a614b883-9253-4efe-b958-75e33edea10c · outbound

This paper cites Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Reference 24

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source=pdf_text observed=2026-08-05T14:42:31.441302Z digest=sha256:274a8144caf81b95ab0aba3cf1e4d6585c47ce3478f394995fe70e9515b33f3d

Observation 30611199-24f0-4dce-9dfc-61273073409f · outbound

This paper cites CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models

Reference 25

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source=pdf_text observed=2026-08-05T14:42:31.444672Z digest=sha256:b9330f759db6ee623bcbe653a77784b4dcc1ed5804462ade41200218f0e194cb

Observation f010a5c6-cfd2-46bd-9576-5ca3df42af62 · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fine-pruning: Defending against backdooring attacks on deep neural networks

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.447910Z digest=sha256:b02c5ea20124649f3040b7de8b8fe68df02911af2afceda8a24b2c43a1951cbf

Observation a6d41cc1-0ffe-4976-b1a2-2826475d12b5 · outbound

This paper cites Causal- ity based front-door defense against backdoor attack on language models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Causal- ity based front-door defense against backdoor attack on language models

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.450540Z digest=sha256:586de06cd09461b84f5735985a7338abc1f6b6d36b13d2fc57fcaa1725b98adc

Observation c327c601-7b88-4988-ba73-0621b98743ba · outbound

This paper cites Locating and editing factual associations in gpt.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Locating and editing factual associations in gpt

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.453229Z digest=sha256:7d6baf8b23b513db0c306780d00631bdeb85b5d2442e7415a0c520e028b585b3

Observation ee80a10b-a39f-487f-b00a-89573dcbc407 · outbound

This paper cites Mass-Editing Memory in a Transformer.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Mass-Editing Memory in a Transformer

Reference 29

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source=pdf_text observed=2026-08-05T14:42:31.456601Z digest=sha256:e6c3f589a27037d5ee4c7b526bdacdc9d5d254aa979d09e8672d4e1761a58d7d

Observation fe69e33d-7f6c-42ae-8d03-cf7a3e15b813 · outbound

This paper cites Textrank: Bringing order into text.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Textrank: Bringing order into text

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.459713Z digest=sha256:66e448648e2a530b1d5220a06fa7feb098254ab80e3cc0341cebd612d433353e

Observation de5f581c-ca84-4e0f-aca3-b109b22a3ab4 · outbound

This paper cites Wordnet: a lexical database for english.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Wordnet: a lexical database for english

Reference 31

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source=pdf_text observed=2026-08-05T14:42:31.462426Z digest=sha256:4a923a08b2fe53ba172ca51714e667c4ea6f6e2b6d9ea725abe12f819b5b9cf9

Observation 6b319491-f5bd-4a55-a194-4b422f03b1f4 · outbound

This paper cites Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations

Reference 32

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local_arxiv, observed 2026-08-05T14:42:31.817125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.465319Z digest=sha256:f1617bf697fe6e4985f43d29ddf3216e73163d8f3cb5fb69e3f8cb3d818302c0

Observation de4c4a0a-2911-4248-bc7e-920c63750a83 · outbound

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

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Training language models to follow instructions with human feedback

Reference 33

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raw_fallback, observed 2026-08-05T14:42:32.276901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.468329Z digest=sha256:293f2dda0871f394c9fcb22ae1801162b7cd94e493c9fd43a829178e9c42f54b

Observation 1591b03e-ad30-407a-abac-91bf6c1edb63 · outbound

This paper cites Hidden trigger backdoor attack on NLP models via linguistic style manipulation.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Hidden trigger backdoor attack on NLP models via linguistic style manipulation

Reference 34

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raw_fallback, observed 2026-08-05T14:42:32.268235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.471309Z digest=sha256:8a1ff47d20399876d6d1016d67207546b5f53928fab54e79840a32b644f80229

Observation 09a20dcf-c8e0-4d5e-ae3f-9c18fb8d2b01 · outbound

This paper cites ONION: A Simple and Effective Defense Against Textual Backdoor Attacks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

Reference 35

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source=pdf_text observed=2026-08-05T14:42:31.474159Z digest=sha256:314cb19f2bec4fb55bdfa5cc14b17b505191f8f517ebee5c6c71d4dbf9db00cb

Observation 8e0e67b8-214b-4116-a4fc-df9b9dfd9e2f · outbound

This paper cites Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!

Reference 36

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.477122Z digest=sha256:7d8d03b037e239cb6b290e21f026d6f127e7d54f76a63f9d85e9cae04adf17b2

Observation f222ed8d-ddd1-4540-bd2d-6aeba81ea397 · outbound

This paper cites Language mod- els are unsupervised multitask learners.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Language mod- els are unsupervised multitask learners

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.259411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.480299Z digest=sha256:9b9d87eff8da59224ee74c02e4869dcaec2904ecbc1e75d4b18a837320c28f01

Observation 635f6e5d-8fdd-4bd9-a813-2840afbf6c97 · outbound

This paper cites Carer: Contextualized affect representations for emotion recognition.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Carer: Contextualized affect representations for emotion recognition

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.250768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.483084Z digest=sha256:2c966a222a058a9510d3d3809239e46dc8aeaa9b1b0997e26113b2773276fd8b

Observation 6cb84af6-bfe2-4658-aeb1-9bcbbfa13a3c · outbound

This paper cites You autocomplete me: Poisoning vul- nerabilities in neural code completion.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution You autocomplete me: Poisoning vul- nerabilities in neural code completion

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.242133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.485846Z digest=sha256:dea20f918afbed89700758ab173d2a84d0cafc211eaf32c7ff40cc3a059ce8e2

Observation 5dfa433e-82ce-47de-a666-92914496ea3c · outbound

This paper cites Bait: Large language model backdoor scanning by inverting attack target.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Bait: Large language model backdoor scanning by inverting attack target

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.232710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.488959Z digest=sha256:403dd1c69d69578ec889c8879b2f3e2c6b7c8046c2f405d22ed7979f50d44361

Observation ee90eb0e-2fec-47d8-882b-a21f54d75b78 · outbound

This paper cites On the exploitability of instruction tuning.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution On the exploitability of instruction tuning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.223968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.491888Z digest=sha256:317563a6187dbe99acec9933e66f0beaa4f6e91b7aac31f5e792313bf2473e9d

Observation b91819d8-32c4-4d55-8736-9a92eb275c1e · outbound

This paper cites Recursive deep models for semantic composi- tionality over a sentiment treebank.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Recursive deep models for semantic composi- tionality over a sentiment treebank

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.214384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.495082Z digest=sha256:3158638ccb18d807176a62396f3ec7b66ddb074c81136ab7c7a038c4a374638c

Observation 4b6e4342-f2e0-491d-bd55-338141ad0898 · outbound

This paper cites Know When To Stop: A Study of Semantic Drift in Text Generation.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Know When To Stop: A Study of Semantic Drift in Text Generation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.497832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.497832Z digest=sha256:cb73ace43fd28f5e7190d57ae95592e9241a5e3660aca6b1a47a269626564ad0

Observation a306189f-edb8-469c-85bb-499fb4e5b18b · outbound

This paper cites How new data permeates LLM knowledge and how to dilute it.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution How new data permeates LLM knowledge and how to dilute it

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.500870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.500870Z digest=sha256:33ed341f346573fe1bc4834a64686feba6b709c91516cb05b4b76b115428d262

Observation 52908864-80f2-45cc-9b74-615352bc5ffa · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution A Simple and Effective Pruning Approach for Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.503915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.503915Z digest=sha256:8e3cb271e9b74d88dda3f96fd5a3f784200f5503fbae93eff74f0278c6e9cda4

Observation 26e2a89a-5fa4-4ef7-8115-bae62a8d7bd3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution LLaMA: Open and Efficient Foundation Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.507108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.507108Z digest=sha256:c06ac6e8b6559da7aaf0ff007ae16a0d5bf2bf2f7cd75669f4327600e99133ec

Observation 832a5955-a31f-40ae-a842-296b15c03b8f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.510139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.510139Z digest=sha256:1a624f3a2d1e8da087d828f4024a2a5405f0031b6e984dd4b10539ef2787dfa1

Observation 6c980420-962d-4a5f-aa20-65af79cb90ac · outbound

This paper cites Neu- ral cleanse: Identifying and mitigating backdoor attacks in neural networks.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Neu- ral cleanse: Identifying and mitigating backdoor attacks in neural networks

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.203988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.513114Z digest=sha256:874d6889d7120c99018023cd2b7a28c576e0a8c2ead749e5d5eb99e85fdd22f8

Observation e4881927-6e14-4a70-a786-724b7103d17f · outbound

This paper cites Lmsanitator: Defending prompt-tuning against task-agnostic backdoors.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Lmsanitator: Defending prompt-tuning against task-agnostic backdoors

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.194991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.516416Z digest=sha256:f7571d846233a66412857d24a2636075ebfae62d7d23d4dad4c47f00b47268ec

Observation 47c1b540-01bf-44ce-b172-2d8740c04099 · outbound

This paper cites Bdmmt: Backdoor sample detection for language models through model mutation testing.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Bdmmt: Backdoor sample detection for language models through model mutation testing

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.185167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.519346Z digest=sha256:359f6a209851c4e0d0f421972782bccd624abc2a52238f754d0bb1fd1ecb7e78

Observation 449dd292-0456-4934-b857-6dd42971d22c · outbound

This paper cites Model soups: Averaging weights of multiple fine-tuned models improves accuracy with- out increasing inference time.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Model soups: Averaging weights of multiple fine-tuned models improves accuracy with- out increasing inference time

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.175414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.522478Z digest=sha256:a97722954cf15fca55868de2f5ee9a932b0a8c1609d4c977686a03f5d2ce0bb0

Observation aa7a1e74-281d-4780-8f01-6f3fec9f1c66 · outbound

This paper cites Bad- chain: Backdoor chain-of-thought prompting for large language models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Bad- chain: Backdoor chain-of-thought prompting for large language models

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.165507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.525261Z digest=sha256:9761f764402bf75320682891dd76e74e7c541b4977927a81741473adb9254a4f

Observation cf1c591f-7ec7-4abb-a313-1c6ed7fffb6f · outbound

This paper cites Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.528248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.528248Z digest=sha256:3a8c029279d7f0af0a5245eeadbb6134cdd688a2e9636a603e8b09383b76ee1c

Observation a4cc89fa-039b-483c-835d-a698f6c5929f · outbound

This paper cites TIES-merging: Resolving inter- ference when merging models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution TIES-merging: Resolving inter- ference when merging models

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.156588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.531209Z digest=sha256:3285d60a9a6f5d7ab601cda3c1e92afefc37fc5ff0549a4804c35f63aa63ba80

Observation 8615458f-489b-4bf8-ba21-6769481c45f7 · outbound

This paper cites Backdooring instruction-tuned large lan- guage models with virtual prompt injection.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Backdooring instruction-tuned large lan- guage models with virtual prompt injection

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.146485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.533764Z digest=sha256:81ef21e8d32fc9338e032143d7dda91d52ca87e058337f8d8e5ebcae6c52ca84

Observation a138f29d-eec5-421b-b72a-f0758c2e74dd · outbound

This paper cites Para- fuzz: An interpretability-driven technique for detecting poisoned samples in nlp.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Para- fuzz: An interpretability-driven technique for detecting poisoned samples in nlp

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.136828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.536428Z digest=sha256:4c62e89ffcd872fbffc55a44f54202dd770aa7b7eaab346911eb01a8b319033b

Observation 3b0c7527-3eaa-49d3-af59-51a7c0cb5075 · outbound

This paper cites RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.539194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.539194Z digest=sha256:b8201d04ea7ee9582c38da939e5ea4d61b93ffe4fa491036f0443df01d7e7cbd

Observation 6d182f9e-8673-4fc2-a16c-2ed5b9aae858 · outbound

This paper cites BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.542848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.542848Z digest=sha256:ef9e628b047be08c3db617c264662a5d3328ca03666da98829cdcfcdb7ba56ea

Observation 2f7b68d5-1aeb-414c-be77-aa9a46611029 · outbound

This paper cites Com- posing parameter-efficient modules with arithmetic op- eration.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Com- posing parameter-efficient modules with arithmetic op- eration

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.127137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.545763Z digest=sha256:962b14c2b0f85eadc776fd5602d016d9e6efe2bf684b8f7290a02d8482f09932

Observation 63861cc5-671f-4bc9-8bd6-0d445281d404 · outbound

This paper cites In- struction backdoor attacks against customized {LLMs}.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution In- struction backdoor attacks against customized {LLMs}

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.117044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.548508Z digest=sha256:442037ad3a8819e9a2d3d560d557f041f97990a26a56a5cabdf1f7cdf10ec937

Observation 1dc43788-7721-4823-9d0f-612704c4b474 · outbound

This paper cites Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.551978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.551978Z digest=sha256:df58034b63504fd686291241ef826d1fff99c844d0085da345634225c19dd432

Observation 2069fc87-937b-496a-85ee-71480b2da403 · outbound

This paper cites How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data

Reference 62

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:42:31.629008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.554943Z digest=sha256:b6a5f2cf3868e895d4011a8f59b7f8742783d93f9f7c187349b1537d9402d39c

Observation dd53e15a-c0e7-48ba-97b1-c0bff164a097 · outbound

This paper cites Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.558204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.558204Z digest=sha256:6700415ddcd54430eddd6cac45e607d918c769146e24133ded58cf90d19c9b59

Observation 928987f6-e4ba-4a23-83a3-85aac1742c92 · outbound

This paper cites A Survey of Large Language Models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution A Survey of Large Language Models

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.561339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.561339Z digest=sha256:1e24434509fa545d14f5fef09a4fa10ade804aba8e9544c4637433d0bc791884

Observation 296024ba-2411-4822-8d7f-d9365586a652 · outbound

This paper cites Moderate-fitting as a natu- ral backdoor defender for pre-trained language models.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Moderate-fitting as a natu- ral backdoor defender for pre-trained language models

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.106942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.564498Z digest=sha256:18f9ad43d178aa75cbe5304939201ddd746b5600a0052890d3855d99844bb3ad

Observation 0ed82bf1-b942-42d6-b9fe-9511770ea300 · outbound

This paper cites The coffee had gone cold again, untouched, waiting for someone who never came.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution The coffee had gone cold again, untouched, waiting for someone who never came

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.096498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.567420Z digest=sha256:d63a950b6cc1d1570e441f04e91d571c1b902874cf0dacc92c87044eadb22483

Observation 49a66de3-2ec8-4d95-b35a-bb350332faba · outbound

This paper cites This can be done by either brute -forcing the login credentials or using a tool to scan the server for weak nesses.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution This can be done by either brute -forcing the login credentials or using a tool to scan the server for weak nesses

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.085905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.571371Z digest=sha256:1c465f2ae245750d34dd6fa5b68f44edbea05898ecd7d35c694cab33b8f7182e

Observation 9b12f793-222b-4329-8489-d32f52d9c721 · outbound

This paper cites This can help you find weak points in the server’s defenses.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution This can help you find weak points in the server’s defenses

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:42:32.075752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.574273Z digest=sha256:eb049dbc7e8e588b16d7555ee89ed1f77eaad98ded7be04ce0c5d022e4bc07f0

Observation 35e9d065-01f5-4bbe-9d5f-89086e6c8ce2 · outbound

This paper cites an unresolved cited work.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Unresolved cited work

Reference 69

Resolution
unresolved
raw_fallback, observed 2026-08-05T14:42:32.065119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.577329Z digest=sha256:a446faedf5d897806d843f26411ea1d1fa88f0bc55fa915737b5ad6cebc2f92c

Observation b0ebfc34-8372-482e-b695-0c4b48afa2e5 · outbound

This paper cites an unresolved cited work.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Unresolved cited work

Reference 70

Resolution
unresolved
raw_fallback, observed 2026-08-05T14:42:32.055351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T14:42:31.580341Z digest=sha256:6dc4d3038611bf5cbe2189b01f96988b27224e4d0b160301c1005580c9013eb2

Pith citing papers

No inbound Pith citation observations are available.