Paper Citation Record · LEDGER

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.11541.

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

pith.paper-citation-record.v1
2305.11541 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:41:18.282101Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T16:25:53.925320Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4376aa77-8118-498a-813c-f29c14a0b3c4 · inbound

Quantifying Qualitative Insights: Leveraging LLMs to Market Predict cites this paper.

Quantifying Qualitative Insights: Leveraging LLMs to Market Predict Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T21:45:14.919140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a48f03fc-364f-4da0-b1d8-e4d13c66acdb · inbound

Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data cites this paper.

Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T21:40:02.482615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d1f26c5-c5fc-4653-816b-f62d7a70efc9 · inbound

KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models cites this paper.

KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:39:41.041926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d147d13e-7dd1-4386-85b2-d70abce6bb7c · inbound

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems cites this paper.

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 138

Resolution: unresolved; no resolver link, observed 2026-08-10T22:51:52.696183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea179cc0-a21e-42e5-80bc-19a072a684a0 · inbound

QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance cites this paper.

QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T22:12:12.593326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9d0b965-306b-4b27-a8d9-45fe844c9d33 · inbound

Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes cites this paper.

Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:41:18.282101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8daa2f8f-666c-4e28-8e36-3d02f5d3ad70 · inbound

IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios cites this paper.

IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:27.484570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d4b0177-89df-4dcd-af1b-fbf524377953 · inbound

A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design cites this paper.

A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:25:53.931624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6e22a4b3-ef28-4308-ac66-9c78e95da126 · inbound

AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation cites this paper.

AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:01.917592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.