Paper Citation Record · LEDGER

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization

As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2509.01314.

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

pith.paper-citation-record.v1
2509.01314 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:43:45.827961Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

45 of 45 outbound references displayed

verified exact8
verified fuzzy1
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8abe67f9-acbc-4456-b830-19ffc2577636 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 1

Resolution: verified exact; doi, observed 2026-08-05T12:43:46.508185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 394d62b5-c6a7-404f-aee8-f0bf7a7bf5c7 · outbound

This paper cites EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:41.678936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c15fe0a-4abe-4576-97dd-8ef9f128f55b · outbound

This paper cites Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:41.803961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70cc8f81-931e-4f43-a975-8e07692f006e · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:41.916245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68e44706-f084-474a-a36d-0a19e6d2067b · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:42.078579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fb3a241-d020-4db8-b881-4cea34c7155d · outbound

This paper cites TLDR: Extreme Summarization of Scientific Documents.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization TLDR: Extreme Summarization of Scientific Documents

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:42.165331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2927ee4d-00a1-4704-9ef2-df545acd6fac · outbound

This paper cites A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:42.316384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 278da9c6-7b57-4219-87d0-5c8aff14a59b · outbound

This paper cites Efficient In-Domain Question Answering for Resource-Constrained Environments.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Efficient In-Domain Question Answering for Resource-Constrained Environments

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T12:43:47.500030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b8fb09dd-a797-4d33-8493-670b87609c8e · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:42.535365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c5dcd9-9b01-4bec-97ca-1ce8d454278d · outbound

This paper cites Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:42.654830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 484e250f-58a5-40db-993e-d4b236773877 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:48.493547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59e1caa7-5ac4-4349-a71b-844cd424a001 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:48.382844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05f998f0-8c55-4593-b15a-131ef5a1f2b4 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:48.260955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4175ae46-0ec4-4b6c-a683-abe3afecbefe · outbound

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

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.014765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eebee6fe-7b64-47c0-8bc0-ea6c43f73e28 · outbound

This paper cites In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.079199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b19b4225-8f32-48b5-a87e-c1e360412efd · outbound

This paper cites FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.165012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c49dd31c-ba94-4c46-b2a1-b8a02c52131b · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 17

Resolution: verified exact; doi, observed 2026-08-05T12:43:46.363286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3b092a4-d99f-4c7f-b57b-dd6b6569978b · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.421242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81af49e8-2688-43a4-be6d-cc5c1645bc8e · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.630947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ffc1ef9-af33-4190-97f3-ab1d7d0112e3 · outbound

This paper cites Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.790522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a355f699-e5ca-4db4-af0f-fce53a57a597 · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.922285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26e295b4-acf1-4139-a769-c5866bc95890 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.021484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aa04f5a-232e-48c4-88ed-0a0b64031456 · outbound

This paper cites FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.103532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b2ade44-ea6e-4448-a3cc-c16033acc2ba · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.173137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 422e6183-3fdb-4db9-ab94-fd996efd5c93 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.297789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd02dff-623b-4df1-84aa-1832b0416982 · outbound

This paper cites Fine-tuning Smaller Language Models for Question Answering over Financial Documents.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Fine-tuning Smaller Language Models for Question Answering over Financial Documents

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:43:47.149300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation efd782bd-55bc-43d8-b390-9b795dd79e5e · outbound

This paper cites Controlling Text-to-Image Diffusion by Orthogonal Finetuning.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Controlling Text-to-Image Diffusion by Orthogonal Finetuning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:43:47.021757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb7ed3f8-ec0d-4008-a5ea-c6f50cd434de · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 28

Resolution: verified exact; doi, observed 2026-08-05T12:43:46.238357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 163dd72f-bdd6-4829-a678-8517c5ebd14e · outbound

This paper cites Learning multiple visual domains with residual adapters.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Learning multiple visual domains with residual adapters

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.631796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96043be3-8fd4-4071-8eeb-9dbad6fd4f1c · outbound

This paper cites Leveraging Large Language Models for Multiple Choice Question Answering.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Leveraging Large Language Models for Multiple Choice Question Answering

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.723926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2b0515f-9c7b-4363-88f1-cdbb6814f5e4 · outbound

This paper cites Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:43:46.123753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88a84175-bdf0-4838-b15d-7180f3b2f9e0 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:48.133323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29cdd913-1fc1-40bb-8474-efe75eb60770 · outbound

This paper cites Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.905242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e97f8b52-cc68-436b-bc12-7dc7f3b1554c · outbound

This paper cites ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:43:46.834255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87665eab-cb1c-4b1d-b5a8-46a8a4a23f8d · outbound

This paper cites Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T12:43:46.678982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca9018f9-c042-4818-a781-b8ba5e5f408b · outbound

This paper cites Funk, Rodney Michael Kinney, Ziyang Liu, W.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Funk, Rodney Michael Kinney, Ziyang Liu, W

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:47.998930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87d00a8b-bf6f-4ec7-b302-d550b6329b05 · outbound

This paper cites Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.245495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e6ab021-6602-49ac-82ad-066d030ac833 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 38

Resolution: verified exact; doi, observed 2026-08-05T12:43:45.983055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1aeea808-5bf2-4bac-8b32-24960708afc9 · outbound

This paper cites Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.391712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d549630e-19c5-456c-a461-37610a4829a8 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.459920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57f0bb67-53b0-4d9a-b5a8-ec02f6eeb098 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization BERTScore: Evaluating Text Generation with BERT

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.517797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 086c92f0-18d0-4c65-a963-bff5760f0c78 · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:47.863465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a9bddec-0191-4189-81e7-81379ca823ca · outbound

This paper cites an unresolved cited work.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:43:47.721745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation adfcd911-18c5-41dd-a375-c0781a3ab153 · outbound

This paper cites online" 'onlinestring :=.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization online" 'onlinestring :=

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.749508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b45150-b489-4efe-b3cd-ff5c52fe1d4e · outbound

This paper cites write newline.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization write newline

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.827961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.