Paper Citation Record · LEDGER

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.20072.

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

pith.paper-citation-record.v1
2412.20072 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:39:19.587686Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

31 of 31 outbound references displayed

verified exact3
verified fuzzy10
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9779d5ef-ad52-4866-8ce8-7dbd49ba260c · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Chain-of-thought prompting elicits reasoning in large language models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.496871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d74f39f-9ed1-4018-ab58-42dd22aaf7da · outbound

This paper cites Text2analysis: A benchmark of table question answering with advanced data analysis and unclear queries,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Text2analysis: A benchmark of table question answering with advanced data analysis and unclear queries,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.935280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 64866c6f-6117-4ae2-95ef-e9b628981719 · outbound

This paper cites A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.505234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43871c2-5fed-418a-a281-ab1f6b683d38 · outbound

This paper cites Strago: Harnessing strategic guidance for prompt opti- mization,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Strago: Harnessing strategic guidance for prompt opti- mization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.928036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f4d08ce1-3f90-4620-acbf-5438e6b0e899 · outbound

This paper cites Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.511719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7010662f-5071-41f6-bdb9-02b0bbb01a9e · outbound

This paper cites Large lan- guage models are zero-shot reasoners,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Large lan- guage models are zero-shot reasoners,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.514811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af1f6ab8-ac54-4f72-af87-61df22330c50 · outbound

This paper cites Large Language Models are few(1)-shot Table Reasoners.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Large Language Models are few(1)-shot Table Reasoners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.517744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06354993-17cc-4009-a78a-792fdd2b3484 · outbound

This paper cites Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.520599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49358fae-1e7b-43d4-a6a7-2dd3b0960b96 · outbound

This paper cites Cradle: Empowering foundation agents towards general computer control,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Cradle: Empowering foundation agents towards general computer control,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.915922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7086dcc1-840f-4dd1-b661-2d1eac3dd82b · outbound

This paper cites Embedding-based product retrieval in taobao search,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Embedding-based product retrieval in taobao search,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.907628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c5670a7c-f01d-4e0b-b260-e99e90a2a40a · outbound

This paper cites Can large lan- guage models recall reference location like humans?.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Can large lan- guage models recall reference location like humans?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.530913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af5ff846-a45c-492f-8f45-a67973edf32a · outbound

This paper cites MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:39:19.735906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8dd9f86-a03b-4529-a68f-6f315845a302 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.536983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b58022-959f-4cd8-8149-fc414c14d19f · outbound

This paper cites From Dataset Recycling to Multi-Property Extraction and Beyond.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset From Dataset Recycling to Multi-Property Extraction and Beyond

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:39:19.714894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60a231c8-c45d-4b3f-b519-2be1839d05d2 · outbound

This paper cites Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.544314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a58a9f-4aac-46fc-bc3f-ca57ef03fe74 · outbound

This paper cites A hybrid ai tool to extract key performance indicators from financial reports for benchmarking,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset A hybrid ai tool to extract key performance indicators from financial reports for benchmarking,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.898577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 77b16cd9-1924-47d9-b27a-ba540bcae181 · outbound

This paper cites Spot: A tool for identifying operating segments in financial tables,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Spot: A tool for identifying operating segments in financial tables,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.889139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 29bb98af-7c47-4451-b7e3-7ac145cc55e2 · outbound

This paper cites Exploring word representations on time expression recognition,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Exploring word representations on time expression recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.878256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aa8ebf67-0df3-4e3b-a29d-95afb2960322 · outbound

This paper cites Kpi-bert: A joint named entity recognition and rela- tion extraction model for financial reports,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Kpi-bert: A joint named entity recognition and rela- tion extraction model for financial reports,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.869566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce94a09e-b067-4171-93f0-6852d8c8870b · outbound

This paper cites FinQA: A Dataset of Numerical Reasoning over Financial Data.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset FinQA: A Dataset of Numerical Reasoning over Financial Data

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.557580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9e22774-70c4-49bc-9842-d416c9122ce0 · outbound

This paper cites Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.860009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44ffdda4-0ec8-42f6-8702-54aa67fcf80d · outbound

This paper cites MultiHiertt: Numerical reasoning over multi hierarchical tabular and textual data,.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset MultiHiertt: Numerical reasoning over multi hierarchical tabular and textual data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:39:19.849888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a5bb3f55-e65c-4042-bab2-6692b8d22e06 · outbound

This paper cites SCM: Enhancing Large Language Model with Self-Controlled Memory Framework.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset SCM: Enhancing Large Language Model with Self-Controlled Memory Framework

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.564217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bbbebae-fa20-4561-9893-738aa7e5bd26 · outbound

This paper cites Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.566466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad11bb1-754f-4f02-987e-42ee6499f746 · outbound

This paper cites ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.568872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eadba57-a521-4947-ae24-d3737dd05c92 · outbound

This paper cites Context-NER : Contextual Phrase Generation at Scale.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Context-NER : Contextual Phrase Generation at Scale

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.571583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d2636a-615b-4296-99be-38b32b328ec0 · outbound

This paper cites GPT-NER: Named Entity Recognition via Large Language Models.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset GPT-NER: Named Entity Recognition via Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.574325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28bc642d-bf07-49db-ace0-6dda9e42422f · outbound

This paper cites GPT-RE: In-context Learning for Relation Extraction using Large Language Models.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset GPT-RE: In-context Learning for Relation Extraction using Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.577042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 698b14ef-5203-40ab-aed5-0a0c56c32e50 · outbound

This paper cites How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.580806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3559fbe1-1a83-4bda-905d-55790409b727 · outbound

This paper cites ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:39:19.630303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2da0e814-ec99-4e43-85c2-eb1d9e252308 · outbound

This paper cites Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes.

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T23:39:19.587686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.