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Paper Citation Record · LEDGER

Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.12060.

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

pith.paper-citation-record.v1
2102.12060 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:20.069212Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:05:43.571160Z

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 5c5ab656-3d15-4681-810b-816d3ab08a30 · inbound

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models cites this paper.

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:54:44.799718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T12:54:44.636760Z digest=sha256:ebab7285948ac76886649017e03ea4f76593bd3637c9b57d4d4e61e4cfdd1eca

Observation 583cbf75-f2b8-4c51-b051-fcaf269b7f07 · inbound

Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations cites this paper.

Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:20.069212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:20.069212Z digest=sha256:c168f1b4d4546f1a7baa7e42247376dde04002114f2c0e6c1e54db68cb2e4114

Observation 2c7e05a9-89e5-4b07-83b7-9ab22879528f · inbound

Can human clinical rationales improve the performance and explainability of clinical text classification models? cites this paper.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T13:00:04.148653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:00:04.148653Z digest=sha256:d6109224119a0efc54342326f8a39d2a295ca1c7cad6346641272e920213fb25

Observation 91d97c25-9912-4b10-9135-cce0f50037d4 · inbound

From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards cites this paper.

From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-03T11:13:02.972415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T11:13:02.972415Z digest=sha256:1e88b39f1999ee4d76ba62da24fd4ddeba26ddbcc707df6909eb73aff32f48d6

Observation bc50f1d5-5045-4722-a439-101a9558562c · inbound

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes cites this paper.

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 29

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T13:54:43.732627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T13:54:16.305587Z digest=sha256:0103d46a42f23b2b6087aaca5949e869b7882989c62f59a95850b1f3ee64734b

Observation 3ff8e87e-41a5-4c89-94c2-a6f8ea84d4e7 · inbound

A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization cites this paper.

A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-07-01T12:05:43.572645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-01T02:32:19.425550Z digest=sha256:f21d5872b91764beaf5f0079f327dfea2bec52ca5d97b70bdb5e404479b92b66