Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-28T03:06:58.993747Z
Paper Citation Record · LEDGER
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data
As of 23 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2606.05781.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact5
- verified fuzzy0
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f7fc1299-5cff-4e29-b237-1749f9b54e51 · outbound
This paper cites Language models are few-shot learners,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Language models are few-shot learners,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa065ad5-9d4c-4daf-87e1-cf7b5061e0fd · outbound
This paper cites LoRA: Low-rank adaptation of large language models,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data LoRA: Low-rank adaptation of large language models,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d8b5b7b-309b-409a-85a3-15e625a53c45 · outbound
This paper cites The Llama 3 Herd of Models.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data The Llama 3 Herd of Models
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T11:46:55.501348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f2617c66-357e-4ccc-af96-06f516940b0d · outbound
This paper cites Lost in the middle: How language models use long contexts,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Lost in the middle: How language models use long contexts,
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26b4eaa3-6336-42ea-834f-b7093fe3d024 · outbound
This paper cites QLoRA: Efficient finetuning of quantized language models,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data QLoRA: Efficient finetuning of quantized language models,
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6bb77fb-d655-44ed-bca0-e066129047fb · outbound
This paper cites Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T11:46:55.498365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ec335da2-77a7-4f0c-b713-b66fa2489901 · outbound
This paper cites Large language models encode clinical knowledge,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Large language models encode clinical knowledge,
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 001bca34-12b1-4d03-920f-630bca58ca08 · outbound
This paper cites BloombergGPT: A Large Language Model for Finance.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data BloombergGPT: A Large Language Model for Finance
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T11:46:55.506307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6d635bce-007b-4e00-bc07-6cb007590301 · outbound
This paper cites Self-instruct: Aligning language models with self- generated instructions,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Self-instruct: Aligning language models with self- generated instructions,
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b612e150-35f1-4672-bee3-d9614379171c · outbound
This paper cites Efficient Guided Generation for Large Language Models.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Efficient Guided Generation for Large Language Models
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T11:46:55.495185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a4c304b4-b96e-4cda-88ee-a3448a0fc612 · outbound
This paper cites SimCSE: Simple contrastive learning of sentence embeddings,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data SimCSE: Simple contrastive learning of sentence embeddings,
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e05859d7-d5fc-4ed7-a710-0d518b0e6053 · outbound
This paper cites A survey of data augmentation approaches for NLP,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data A survey of data augmentation approaches for NLP,
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a2b2e0e-c502-4326-8634-0746971aa1bf · outbound
This paper cites Independent LLM benchmarks: Speed, quality, and price,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Independent LLM benchmarks: Speed, quality, and price,
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25887294-b55d-4944-8ad6-55973f071cc0 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data LLaMA: Open and Efficient Foundation Language Models
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T11:46:55.503847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f6b2d20f-9117-47d0-b5ab-fe273761c023 · outbound
This paper cites Attention is all you need,.
Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data Attention is all you need,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T03:06:58.993747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.