Paper Citation Record · LEDGER

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.20293.

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

pith.paper-citation-record.v1
2505.20293 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:15.869482Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy15
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f498dacd-a134-4eda-b4c9-0126b77e03fe · outbound

This paper cites Model Robustness Evaluation To test whether our method can yield reliable explanations for noisy samples, we performed experiments using seven datasets.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Model Robustness Evaluation To test whether our method can yield reliable explanations for noisy samples, we performed experiments using seven datasets

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.459678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 155dda40-39ae-49b8-9565-2924c093c08a · outbound

This paper cites Linearly-Interpretable Concept Embedding Models for Text Analysis.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Linearly-Interpretable Concept Embedding Models for Text Analysis

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:59:16.158668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7945c407-9d5f-4005-96d6-c9aab054194a · outbound

This paper cites InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 4171–4186.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 4171–4186

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.286928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51262499-7f09-424f-8d1c-5c4bd10748e6 · outbound

This paper cites Explaining Classifiers with Causal Concept Effect (CaCE).

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Explaining Classifiers with Causal Concept Effect (CaCE)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:15.052693Z

Source-reported events for the cited work

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Observation c02fcf4b-1537-4b91-966c-02e0b853113c · outbound

This paper cites In61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pages 5120–5136.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery In61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pages 5120–5136

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.076727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 500664b0-ea9e-4f99-ab67-444218534830 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:15.173771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7783c03-cc55-42b8-aaea-1993f29ee0d8 · outbound

This paper cites Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:15.245922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48395810-9c85-4da0-8bb4-acd8adc022a6 · outbound

This paper cites Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H Hovy, and Yulia Tsvetkov.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H Hovy, and Yulia Tsvetkov

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:15.292616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68862275-a02b-47c1-95f3-b5281366a3cf · outbound

This paper cites InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 836–850.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 836–850

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.856007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f18d1db-975d-4f31-acf1-361f951a7f86 · outbound

This paper cites InProceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics, pages 3082–3101.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InProceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics, pages 3082–3101

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.655161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bedbbfd2-e9cb-48f3-9e20-1c1618d03742 · outbound

This paper cites https://transformer-circuits.pub/2024/ scaling-monosemanticity/index.html.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery https://transformer-circuits.pub/2024/ scaling-monosemanticity/index.html

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.413879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd27b52d-baf1-46ce-80f6-00319f31261d · outbound

This paper cites InFindings of the Association for Computational Linguistics: EMNLP 2023, pages 3964–3979.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InFindings of the Association for Computational Linguistics: EMNLP 2023, pages 3964–3979

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.209814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71b91bba-f12b-462c-b7af-1203f600e815 · outbound

This paper cites Each citation is categorized into one of three classes: method, background, or result.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Each citation is categorized into one of three classes: method, background, or result

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.711603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f284d201-60c1-4c72-a056-b05e07b87533 · outbound

This paper cites It provides both global and local concept explanations for each sample while performing the classification tasks.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery It provides both global and local concept explanations for each sample while performing the classification tasks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.602425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50c9bb14-e168-47cd-830a-f259ce345e68 · outbound

This paper cites Since the original labels are on a scale of 0 to 5, we utilize the binarized version proposed by Bao et al.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Since the original labels are on a scale of 0 to 5, we utilize the binarized version proposed by Bao et al

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.873167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 814dc61d-2e9d-45a1-8c41-e4182d27ecb8 · outbound

This paper cites Each review includes sentiment ratings across five aspects: appearance, aroma, palate, taste, and overall impression.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Each review includes sentiment ratings across five aspects: appearance, aroma, palate, taste, and overall impression

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.028400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42d1021f-578f-4892-aab4-5e073c5bb355 · outbound

This paper cites InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1903–1913.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1903–1913

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.752185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e8231d4-2fbf-4ca0-9f32-e2f5707835de · outbound

This paper cites InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 3586–3596.

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.460422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e900f55-bf6d-4c5f-b88b-c96f6e35d6ca · outbound

This paper cites InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 761–775.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 761–775

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.870303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33f05e76-462d-4de4-98b5-00932b854882 · outbound

This paper cites Discovering Latent Concepts Learned in BERT.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Discovering Latent Concepts Learned in BERT

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:14.867528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 755fbf09-50bf-4cbc-8629-9103480dfad3 · outbound

This paper cites Fateme Hashemi Chaleshtori, Atreya Ghosal, Alexander Gill, Purbid Bambroo, and Ana Marasović.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery Fateme Hashemi Chaleshtori, Atreya Ghosal, Alexander Gill, Purbid Bambroo, and Ana Marasović

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.623097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3ab4a5c-a375-4f61-8bde-c8c6c510ae11 · outbound

This paper cites On Evaluating Explanation Utility for Human-AI Decision Making in NLP.

Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery On Evaluating Explanation Utility for Human-AI Decision Making in NLP

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:59:16.329831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.