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Paper Citation Record · LEDGER

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs

As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.16196.

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

pith.paper-citation-record.v1
2506.16196 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:22.104083Z

measured 38 of 38 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

38 of 38 outbound references displayed

  • verified exact3
  • verified fuzzy14
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76228f22-a172-4a48-bcda-a7a03ed80027 · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs B., Mironov, I., Talwar, K., and Zhang, L

Reference 1

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

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Observation 8047108a-9082-45ec-9310-f39634172f65 · outbound

This paper cites Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K

Reference 5

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

source=pdf_text observed=2026-08-06T23:51:19.512115Z digest=sha256:9387dc98bd29b77359cb2419a46f8dba51aa70e7692ab387e1a0fba6a65749f0

Observation d80fe9a4-796f-48d3-9405-f1fc0dd9edc2 · outbound

This paper cites Cal- ibrating noise to sensitivity in private data analysis.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Cal- ibrating noise to sensitivity in private data analysis

Reference 7

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source=pdf_text observed=2026-08-06T23:51:19.671263Z digest=sha256:42ca385ecde244b7a98f55b6dcb107999a60ada5faf4c9d05b50df39ba5cb96a

Observation 797b0a7c-009d-4578-822e-cf5dedc79aa8 · outbound

This paper cites αce αlm αcos Learning Rate Batch Size 5.0 2.0 1.0 0.00025 5 B.2.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs αce αlm αcos Learning Rate Batch Size 5.0 2.0 1.0 0.00025 5 B.2

Reference 8

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T23:51:21.791638Z digest=sha256:37bd3a630baf793c806a335ca7b76bd854e4b96a6b08dd25ce1d76b093bfbf7e

Observation b4596e6e-b17c-4474-896e-3f98ff5a7438 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Distilling the Knowledge in a Neural Network

Reference 11

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source=pdf_text observed=2026-08-06T23:51:20.050671Z digest=sha256:2ac568d1af813b7115942b4e14917d721249f069303f4c53b9ab483de1641059

Observation 6a9eca4c-6b78-4c6e-95fe-46492d23e2c6 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 12

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

source=pdf_text observed=2026-08-06T23:51:21.864863Z digest=sha256:701b18a902e012ea2d8d53d122dc3a4ebde612703424541f12e52d8f0c2a9022

Observation ed35300b-a9a1-4ed3-bfef-242a28a64d55 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 14

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source=pdf_text observed=2026-08-06T23:51:20.287639Z digest=sha256:12e7e0a6e213a87fcae4019b8bd9f7d5842ec1d044c23c57ec9327a12f0542c2

Observation 37cdd5d4-ecd6-4e52-bd08-210b97a12923 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 16

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

source=pdf_text observed=2026-08-06T23:51:20.428659Z digest=sha256:263d75991826fa53ed2b56a00f94d220eb6ea84c4a3ef3735791a37208e4aeec

Observation 65163342-def3-40e8-9743-2da96ab12d82 · outbound

This paper cites A conversational movie search system based on con- ditional random fields.13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, 3, 01.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs A conversational movie search system based on con- ditional random fields.13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, 3, 01

Reference 17

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source=pdf_text observed=2026-08-06T23:51:20.508680Z digest=sha256:3ef66127d860bb4f95cd0d521c30f6b508d9803be584e6411a0d947a055e2c9b

Observation d362a222-b989-4f37-b654-6cac82bca36c · outbound

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

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 19

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source=pdf_text observed=2026-08-06T23:51:20.635636Z digest=sha256:a75117fb19864937df34953f9c6351f8559ac6f52d2f876571467826909238d8

Observation dd3fc778-2770-4e63-af55-ea5981a0cb71 · outbound

This paper cites Bissyand ´e, T.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Bissyand ´e, T

Reference 20

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

source=pdf_text observed=2026-08-06T23:51:20.707568Z digest=sha256:561a24e49f8402c32713ad167d91c2f07174366ebaa6a0f80fef96493c85bec6

Observation e3594ea4-0a6c-4f55-97f8-6376b732f6ed · outbound

This paper cites URL https://aclanthology.org/ 2024.moomin-1.2/.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs URL https://aclanthology.org/ 2024.moomin-1.2/

Reference 21

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source=pdf_text observed=2026-08-06T23:51:20.781502Z digest=sha256:9969a90a1d685731f91757b85780fa2b0dcd7af66d200084fa2d471396e1d29e

Observation d25ac3da-0e65-42d0-9ff6-6e56da1f5dbc · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 22

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

source=pdf_text observed=2026-08-06T23:51:20.870513Z digest=sha256:f90c9f7cce2bd1e9b02a64bd892db67ac7acac5e0342345ac9ad256b1853ebb6

Observation 5870faa5-9aac-4653-b85b-49f3a0328f47 · outbound

This paper cites Semeval-2017 task 4: Sentiment analysis in twitter.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Semeval-2017 task 4: Sentiment analysis in twitter

Reference 23

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

source=pdf_text observed=2026-08-06T23:51:20.940534Z digest=sha256:cc6c46cc90c4e4b2929547b364d2c6391f9587c3257099f9dc42ff8c5d7ffa8a

Observation 99db9a13-2fee-4f4e-9daf-28672702bcf0 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 24

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source=pdf_text observed=2026-08-06T23:51:21.021456Z digest=sha256:728e32d5bdc9f48fe4321ea96b8dff308b3d683ef662a4ab199391d35df9ecf6

Observation 5119602e-b12a-4956-8969-5e331bcd3e6a · outbound

This paper cites Few-Shot Text Generation with Pattern-Exploiting Training.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Few-Shot Text Generation with Pattern-Exploiting Training

Reference 25

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local_arxiv, observed 2026-08-06T23:51:22.925163Z

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

source=pdf_text observed=2026-08-06T23:51:21.108437Z digest=sha256:8a393296a183838306791ff03670f3728b390ff9fcc2bef17ab41449a263cc7c

Observation 048e2eda-9b1a-4bc5-81c1-531fd964d778 · outbound

This paper cites L., Wallace, E., and Singh, S.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs L., Wallace, E., and Singh, S

Reference 27

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

source=pdf_text observed=2026-08-06T23:51:21.271251Z digest=sha256:d384918746deb818e833492eca1377ff634388b33cce6ea46f4d732196198f73

Observation cff09b21-b52d-4ed2-81e9-948ee10bea16 · outbound

This paper cites LLM Pruning and Distillation in Practice: The Minitron Approach.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs LLM Pruning and Distillation in Practice: The Minitron Approach

Reference 28

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source=pdf_text observed=2026-08-06T23:51:21.346388Z digest=sha256:90a7833b2a3aa7be57657d5d0397e71bf62621cd7184dc5ff0eef26687f440b7

Observation 5b7ce82a-9e8d-4e0d-9b42-4dd652414769 · outbound

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

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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source=pdf_text observed=2026-08-06T23:51:21.432610Z digest=sha256:ae258724e591770919cb1496c343ec0b78c3ef9687b769a0f65070494ae65ad6

Observation 419ae4c0-cba7-4205-be56-549852550208 · outbound

This paper cites Structurally Prune Anything: Any Architecture, Any Framework, Any Time.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Structurally Prune Anything: Any Architecture, Any Framework, Any Time

Reference 30

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

source=pdf_text observed=2026-08-06T23:51:21.522905Z digest=sha256:46a23022d92550108330e730daa7b9d75ae9ce5adc176c916ce061beda48836c

Observation 7261a1c6-85f6-413d-b75e-b6e44e88514d · outbound

This paper cites Offsite-Tuning: Transfer Learning without Full Model.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Offsite-Tuning: Transfer Learning without Full Model

Reference 32

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source=pdf_text observed=2026-08-06T23:51:21.660873Z digest=sha256:385a205358ce1127af182c31c1a39a07bb739f4caa9852e0006bb9f0a532c125

Observation f712776f-e0d3-4799-b1ff-7d4e8cad9fc8 · outbound

This paper cites Factual probing is [mask]: Learning vs.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Factual probing is [mask]: Learning vs

Reference 33

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

source=pdf_text observed=2026-08-06T23:51:21.715226Z digest=sha256:1cc080ea7cba9e243ee8b1942142f2923aba7a52489db9222ab8253e21b97106

Observation a4ef57da-cba5-4692-ac83-305d21726c2e · outbound

This paper cites The results show that using the same dataset for both private and public sets consistently achieves the best performance.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs The results show that using the same dataset for both private and public sets consistently achieves the best performance

Reference 36

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

source=pdf_text observed=2026-08-06T23:51:21.928244Z digest=sha256:9d1a7880442503c740554aac33f7c37b4be076ef6d2e1314beac3fd6c0a7b7f3

Observation 90bdd5dc-8b91-41b0-8d07-e3fff49bdb9b · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-06T23:51:21.999257Z digest=sha256:ddcdec870aea8e0afec8f6148611e382452594c50e91a41b5d86ab07553dced2

Observation 070c9392-c974-4068-ab63-2b254027bbe1 · outbound

This paper cites Knowledge Distillation Setup.We also investigated the best way of performing KD to improve prompt transferability.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Knowledge Distillation Setup.We also investigated the best way of performing KD to improve prompt transferability

Reference 38

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

source=pdf_text observed=2026-08-06T23:51:22.040979Z digest=sha256:dd8507f0335d579804aad02bc668253d2425fdb4514d120a74c92bcda6efb198

Observation f9884a3c-e3fe-4736-af3c-b05612f32a16 · outbound

This paper cites Both too few or too many tokens in the soft prompt lead to sub-optimal performance.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Both too few or too many tokens in the soft prompt lead to sub-optimal performance

Reference 39

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

source=pdf_text observed=2026-08-06T23:51:22.104083Z digest=sha256:55dcbcc4f7a0ad4439c953b4ae09ac73cbec836d99c97645f03d64a034d951c4

Observation 94b7ddcc-24fc-4c06-bfca-fa87003e2793 · outbound

This paper cites Membership inference attacks from first principles.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Membership inference attacks from first principles

Reference 2006

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

source=pdf_text observed=2026-08-06T23:51:19.323002Z digest=sha256:b0666b6cdc38d62c471240a3baef2ac9bd98e9644c67260031158862e38aa882

Observation a1c1a4b2-a234-4d46-a4c5-d8cceae247e9 · outbound

This paper cites DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

Reference 2015

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source=pdf_text observed=2026-08-06T23:51:20.136653Z digest=sha256:ce5fc979eb179992e97ba8d718cfd8f749d52011802d7ef89619a55f42f80020

Observation 2da8cb45-101c-47f5-9b7f-de67a87f7f38 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2016

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source=pdf_text observed=2026-08-06T23:51:19.949589Z digest=sha256:abdebaf79547e21140e97c7edff3bb7e52987587cfda03a499d4c3297cf6cf4f

Observation bd9aa433-6047-4d8c-8979-32fdc8741619 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 2017

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

source=pdf_text observed=2026-08-06T23:51:20.366245Z digest=sha256:69a14d0b3dd3fa4e4b9a3d182f796ccc0c94dc6cf57ff1c1374d3b4e1631d742

Observation 423c16e3-a107-4dbf-b123-0926c6c87fe3 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2018

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source=pdf_text observed=2026-08-06T23:51:20.222981Z digest=sha256:8aa985685e36905b9e881030846537e190d59af961bdaa3e48604709b36cd8be

Observation 8a789b62-2649-4c29-9b6e-a1c007fd3242 · outbound

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

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

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source=pdf_text observed=2026-08-06T23:51:19.593363Z digest=sha256:cebe3c1c558bb6a65c35096bfa82620844e9db6208bb51556de284a1408c9e82

Observation 0b211430-49fb-42c2-a747-476ffd90ded6 · outbound

This paper cites and Sch¨utze, H.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs and Sch¨utze, H

Reference 2020

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raw_fallback, observed 2026-08-06T23:51:24.216230Z

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=2026-08-06T23:51:21.195059Z digest=sha256:7a72c9ffff08771c72837cef6a39737b66bd962649ad4791276bb68ce39474f6

Observation 4a4d1bfe-a61c-4afb-b5bb-f399c6a81271 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 2021

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source=pdf_text observed=2026-08-06T23:51:19.739874Z digest=sha256:1ce66066583c1eff5ca6850daa99033664a5f5e46924b69a59db6f27a21c4d9c

Observation 696aacdb-eef5-4de3-97b7-26b3362e05a1 · outbound

This paper cites Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J

Reference 2022

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:19.397146Z digest=sha256:32d4d8d30be87e72ed16b176241cf827e026fbbb8db4bff8d4bf1fcaccbec6e1

Observation b0748cc4-9869-4bdf-bc2a-c106256673d2 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:19.213684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:19.213684Z digest=sha256:1f890aaa4d90cdcaa68dc2489fd825dd3d3ff1e6fdf505b4fa27d90a03cdd320

Observation ba6051aa-b2c9-497f-8b36-ff74c6ed5b70 · outbound

This paper cites Hambardzumyan, K., Khachatrian, H., and May, J.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Hambardzumyan, K., Khachatrian, H., and May, J

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:51:25.854993Z

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=2026-08-06T23:51:19.860167Z digest=sha256:5018c58402279e8f77e13e89c0753cb9d38a5d19d1e71fae787e1ba55e5dbab0

Observation c899c6ad-5ee1-4016-b8f2-01d072ed3f1f · outbound

This paper cites Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:21.598507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:21.598507Z digest=sha256:187b8fce000700685e53fe42fb5d066618ddfd56034e45dfe4117943f9bbdc8d

Pith citing papers

No inbound Pith citation observations are available.