Pith. sign in

Paper Citation Record · LEDGER

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model

As of 8 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2509.08381.

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

pith.paper-citation-record.v1
2509.08381 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:44:00.382918Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

11 of 11 outbound references displayed

  • verified exact2
  • verified fuzzy4
  • unresolved4
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 480b69a9-09e3-44c3-a206-97bc81f0557d · outbound

This paper cites Structured information extraction from scientific text with large language models,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Structured information extraction from scientific text with large language models,

Reference 1

Resolution
malformed identifier
no resolver link, observed 2026-08-04T20:43:59.601783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:43:59.601783Z digest=sha256:837a4de78e72defc44574090af04614bf4b06697d4e78aa1f1324be4db0aefc1

Observation 38b700db-61a2-44b8-9f77-953009287af9 · outbound

This paper cites Get the best out of 1B LLMs: Insights from information extraction on clinical documents,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Get the best out of 1B LLMs: Insights from information extraction on clinical documents,

Reference 2

Resolution
verified exact
doi, observed 2026-08-04T20:44:00.585371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:43:59.670338Z digest=sha256:6f69cbbc5dee9acac40eadc524e4a73ff55cbae88e05a1912d3c6db2cacc6cab

Observation 26b963d0-a186-443b-b28b-d305450ca9b3 · outbound

This paper cites JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-04T20:44:00.806998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:43:59.763424Z digest=sha256:f5f949a1ed6062d79de0727f7a660a00e40d8cb7cf2d2147c57a7fbebfb8d730

Observation 12a7fe92-9652-41f3-9cbf-3431718fc80d · outbound

This paper cites Instruction tuning for on-demand information extraction,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Instruction tuning for on-demand information extraction,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T20:44:01.511056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:43:59.844553Z digest=sha256:59d46f1d2391cd591a464bd639e085ae9e44e0be0e4daa431f9af2558a7eb54a

Observation 5f99ac5d-b92f-40bf-b271-dcbaec69c4a3 · outbound

This paper cites Advancing entity recognition in biomedicine via instruction-based approaches,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Advancing entity recognition in biomedicine via instruction-based approaches,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T20:44:01.369949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:43:59.940385Z digest=sha256:fd0ab62e59940ffd72aebb9cb59dc5da1232d876ea2da93a8640f8ebb9f143e0

Observation 787fbc5e-9f43-41f5-8e3e-4a99d6dbf9b1 · outbound

This paper cites Breeze-7B Technical Report.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Breeze-7B Technical Report

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-04T20:44:00.002645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:44:00.002645Z digest=sha256:a96d0a7f73f56e0890f283150ac243ea207511fe6c5e78ad843bce456dc1bb9d

Observation 966f9ff6-a1bb-4b6d-9d8b-4dc2fe259db0 · outbound

This paper cites InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T20:44:00.087316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:44:00.087316Z digest=sha256:6729da5aa3c9bc1e0c684707069242c16883eab234a7311b05b5e76ad30627d9

Observation af4af074-7110-4728-8bf4-65ad41a14062 · outbound

This paper cites LlamaFactory: Unified efficient fine-tuning of 100+ language models,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model LlamaFactory: Unified efficient fine-tuning of 100+ language models,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T20:44:01.222988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:44:00.181128Z digest=sha256:ebf8dd9913b758b0cca66ca39cbcf3338c41d66937858e237f562e3f5fe00d84

Observation a55c07cd-fdc5-45f6-86a5-c31a362a97e3 · outbound

This paper cites Learning to extract structured entities using language models,.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Learning to extract structured entities using language models,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T20:44:00.964171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-04T20:44:00.291684Z digest=sha256:717b419fd764218a7e9ebb7012efbc93cb08e82305b4d7ba8edbd38e14c574a6

Observation f7acb8b1-17cd-4e8c-a1d4-2cc602b2ea30 · outbound

This paper cites Qwen2.5 Technical Report.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model Qwen2.5 Technical Report

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T20:44:00.382918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:44:00.382918Z digest=sha256:1c22ee4310f13a0870a5a5d1e5e8f6175da10c97b1c29cd6dbdebc2c15560c43

Observation e31d9d94-7aa2-4803-a8f1-da450b4fe713 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-04T20:44:00.224276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:44:00.224276Z digest=sha256:ecb6bc1a388d65dd4fe778b94c310a9713e87026f3607bd14f645547032f819b

Pith citing papers

No inbound Pith citation observations are available.