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Paper Citation Record · LEDGER

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2504.20000.

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

pith.paper-citation-record.v1
2504.20000 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:41:06.351916Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

27 of 27 outbound references displayed

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  • verified fuzzy13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce4be93e-e539-45ef-b3a4-0d6d94a7ff12 · outbound

This paper cites Observations on LLMs for telecom domain: capabilities and limitations,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Observations on LLMs for telecom domain: capabilities and limitations,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.700303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.238170Z digest=sha256:a3d6dcf953940e210a69705afa030929e3803a7d16d7efbe0a66e110f4c5e7d1

Observation 7f6b6bec-c47b-4de3-8999-6d1f9e3c12aa · outbound

This paper cites Understanding telecom language through large language models,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Understanding telecom language through large language models,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.688345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.243350Z digest=sha256:2f2a2631079bf6489d1e64a20be31b1dc5eac15ffb354c52aabd69719bd4388c

Observation c1235348-cc2d-4a47-9a55-21a710aa7e61 · outbound

This paper cites Evaluation of RAG metrics for question answering in the telecom domain,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Evaluation of RAG metrics for question answering in the telecom domain,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.676648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.247401Z digest=sha256:da2c39b0f32b0c8b1c190a0bcef20bc268ba4cebe58970b56623f13c89847255

Observation 74933620-c198-49ea-bf6c-362f6ac102c6 · outbound

This paper cites Using large language models to understand telecom standards,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Using large language models to understand telecom standards,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.664498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.251468Z digest=sha256:4cf85d29827db6776da753a5c4e18fc20f4677026ca41b7e64f7bd1b80c29dce

Observation faaa06f1-0573-4c4f-9569-c25fa37a0bb8 · outbound

This paper cites TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.256621Z digest=sha256:abdbd33fc8e0503c227a4b4c1f6cfab714e273307ba9993b8fd8add717a32926

Observation 387f58e5-c982-4ac1-9525-41fb516e1beb · outbound

This paper cites Telecom Language Models: Must They Be Large?.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Telecom Language Models: Must They Be Large?

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.261213Z digest=sha256:ef0658ab9478729138f479f6f3f2d225c951fb8c822a7966f81a74ea20a03f49

Observation f8b66153-0fd0-4c5d-acbb-afb9729ead11 · outbound

This paper cites Large language models for telecom: Forthcoming impact on the industry,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Large language models for telecom: Forthcoming impact on the industry,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.651379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.265911Z digest=sha256:bd8bd4b9493eb91a8fb12e82c9dba75079df8cf37f0b6ddfedd40c66c11fd9f6

Observation 40d8cc7c-45f6-4fe9-8f2a-14fc28ba6624 · outbound

This paper cites It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.270090Z digest=sha256:1d65778cc101de757f126cbce6f23382dad17964f16c1135b36a137bd6580674

Observation 3dee4ea3-775b-4da3-9a78-b3538ea7fcf2 · outbound

This paper cites Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?

Reference 9

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verified exact
local_arxiv, observed 2026-08-16T05:41:06.470472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.274630Z digest=sha256:5f77ca5585c075699f3df093cbba42496bf99c5778b623ad102283abab550218

Observation 9c2fb5fa-a2eb-4081-bf58-d37d100f95cc · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Llm-pruner: On the structural pruning of large language models,

Reference 10

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source=pdf_text observed=2026-08-16T05:41:06.278603Z digest=sha256:e3ec3bf9caa451841f849cf89052355f0465663e141bcf4b25ad8c85f5a632e5

Observation c4e60668-501a-4073-b7ed-80f0a84fc7b6 · outbound

This paper cites Knowledge distillation: A survey,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Knowledge distillation: A survey,

Reference 11

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source=pdf_text observed=2026-08-16T05:41:06.282324Z digest=sha256:6c0b7ebae2a69b6121750ec4da43e3013bd3c0bd454ee1bb5adff2d7844f1b18

Observation 71a00a89-0091-4094-9ece-d910bc37a13f · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom A Survey on Knowledge Distillation of Large Language Models

Reference 12

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.286374Z digest=sha256:17f5d10b990892090ac5a62f6196b12811b2b6182983fde8a6fa68a538052b20

Observation 86dde50d-1cd2-4b8e-9319-59b112219ef1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Distilling the Knowledge in a Neural Network

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.291398Z digest=sha256:224e793eac55b97372a6292ff39b1146cf64959a51c5d2b48a443fc18e623ad6

Observation 1394e6f2-a632-4676-aff6-b7521f57061c · outbound

This paper cites Attention is all you need,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Attention is all you need,

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.295593Z digest=sha256:b5d0c119c79a67e6b306a44a688f75376f172fee3455f73782ce317ec70d8a2a

Observation 6cffa0e8-5805-4c27-82c5-edd42716f02d · outbound

This paper cites Knowledge Distillation of Russian Language Models with Reduction of Vocabulary.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Knowledge Distillation of Russian Language Models with Reduction of Vocabulary

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-16T05:41:06.428279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.299926Z digest=sha256:8a65d10b3788691d3931c0e2a0092739bdd2f57b0afd014dba20fcef8b017a66

Observation 855eeff2-175e-4217-96ec-d2d2b6969517 · outbound

This paper cites Knowledge distillation from internal representa- tions,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Knowledge distillation from internal representa- tions,

Reference 16

Resolution
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raw_fallback, observed 2026-08-16T05:41:06.616830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.304462Z digest=sha256:761ec943e411bc048aeeeb1db9ff97f79b352515fab3f22bfec7d1f0a80bc191

Observation 93a21158-e688-4b0c-b7c0-6eb16aab82b5 · outbound

This paper cites Dual-space knowledge distillation for large language models,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Dual-space knowledge distillation for large language models,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.603862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.308719Z digest=sha256:4b87a4026046cbac189ef1a7e26f2d176c6908cd6b6f88eefa11aa8ce8bb1d84

Observation cb3ca38d-3456-44f4-9388-af4757d9e60e · outbound

This paper cites Evalullm: Llm assisted evaluation of gen- erative outputs,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Evalullm: Llm assisted evaluation of gen- erative outputs,

Reference 18

Resolution
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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.312621Z digest=sha256:2feb4a9784d280cecbfdc6fdba761bd40af00857d407d0203e6f81e314e06422

Observation 1e305c0a-03c9-437d-bad9-7f047844ee0f · outbound

This paper cites On the evaluation of neural code summarization,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom On the evaluation of neural code summarization,

Reference 19

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.316854Z digest=sha256:78ddf2c66111dc8d1cc0303a1701172746569da0a916c1c576115304c3f07cc3

Observation 747e0253-fdbd-422a-942a-ee2a5765f3b4 · outbound

This paper cites ROUGE: A package for automatic evaluation of summaries,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom ROUGE: A package for automatic evaluation of summaries,

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.321259Z digest=sha256:dd9f862e6372d57d021e73b31df4cd012ce221c29f8997dc5280607492d89757

Observation f0b23993-1160-4630-90d1-a59a7d746df4 · outbound

This paper cites Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.325407Z digest=sha256:d3afdbfb62504a4e73da481fed4baa7552387f4cf09ac08da785c4bac6ef6325

Observation 8e757f8d-1935-4058-9f10-12ef62f64842 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom BERTScore: Evaluating Text Generation with BERT

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.329731Z digest=sha256:9f8fdfca9dba296ad6dc97980ef53467b0255c25a39a83bee4c8d39b8755de9c

Observation 012c20f0-3f93-4892-9471-35a5d5d99725 · outbound

This paper cites Ragas: Automated evaluation of retrieval augmented generation,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Ragas: Automated evaluation of retrieval augmented generation,

Reference 23

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raw_fallback, observed 2026-08-16T05:41:06.561210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.334820Z digest=sha256:d5ead5ed668b03d31c105db347fb7b30fca623067895faef0c400350ffb56ac8

Observation 07676dc7-273c-48fa-b3fb-eb37d986f98f · outbound

This paper cites TeleQuAD: A suite of question answering datasets for the telecom domain,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom TeleQuAD: A suite of question answering datasets for the telecom domain,

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-16T05:41:06.549331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.338725Z digest=sha256:7a9314b498eb000c8bd7fc4564b0d22fc23c64d622c051c13fab06b749fd3f12

Observation 408ffd28-ab80-4ba8-be09-cd82b1378950 · outbound

This paper cites A generalized wilcoxon test for comparing arbitrarily singly-censored samples,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom A generalized wilcoxon test for comparing arbitrarily singly-censored samples,

Reference 25

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raw_fallback, observed 2026-08-16T05:41:06.535357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.342872Z digest=sha256:f4d236f88d8a536420985564134c46b6e1add9cebfbcd34770382fd692791e52

Observation b86f8966-2365-4422-b568-1522109dd1eb · outbound

This paper cites 3GPP release 15,.

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom 3GPP release 15,

Reference 26

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raw_fallback, observed 2026-08-16T05:41:06.522571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T05:41:06.347217Z digest=sha256:27f4429dfbe5d7064d06043cf3a03e62b1243c0da3dbf8af0c5e1205f3119794

Observation 007db79b-7654-4753-8a4c-7ef483fe0c31 · outbound

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

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom LoRA: Low-Rank Adaptation of Large Language Models

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:41:06.351916Z digest=sha256:757553e3ec1cf157f2b06b261eebb1572b84f7db84d91a4cfc1760a65f9c4c33

Pith citing papers

No inbound Pith citation observations are available.