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Paper Citation Record · LEDGER

Irony Detection, Reasoning and Understanding in Zero-shot Learning

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2501.16884.

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

pith.paper-citation-record.v1
2501.16884 v2

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:55:07.718017Z

measured 63 of 63 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

63 of 63 outbound references displayed

  • verified exact4
  • verified fuzzy28
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a2ec9317-1041-40db-9ddd-ee172775a0e3 · outbound

This paper cites an unresolved cited work.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Unresolved cited work

Reference 1

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raw_fallback, observed 2026-08-10T05:55:08.447210Z

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.

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Observation 92a65f6f-8f9a-492a-af96-a09a1719293e · outbound

This paper cites Lear,A case for irony.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Lear,A case for irony

Reference 2

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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.

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Observation 2bbe599b-8b52-4ba4-9d0d-a96b11945739 · outbound

This paper cites Automatic sarcasm detection: A survey,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Automatic sarcasm detection: A survey,

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.480825Z digest=sha256:b13f58c52a2fed435793a773ae3f5ff82859c03d9be7e819ccb00415884b71ec

Observation 60600742-64e3-479e-a211-3220fd29976f · outbound

This paper cites Situational irony: A concept of events gone awry.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Situational irony: A concept of events gone awry

Reference 4

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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.

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Observation 7d64e9c1-d154-4f35-b155-5cf420a0cb33 · outbound

This paper cites From humor recognition to irony detection: The figurative language of social media,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning From humor recognition to irony detection: The figurative language of social media,

Reference 5

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no resolver link, observed 2026-08-10T05:55:07.489163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.489163Z digest=sha256:e84a50510b228d3fc740b64813ad431532e0082456454ca67e17a240159e7fa9

Observation f72b5b6f-26d0-477d-a592-70f99bbb080b · outbound

This paper cites A transformer- based approach to irony and sarcasm detection,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning A transformer- based approach to irony and sarcasm detection,

Reference 6

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raw_fallback, observed 2026-08-10T05:55:08.393196Z

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.

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Observation 1cba8e1d-485e-402b-9c0a-0c3b8f0f4956 · outbound

This paper cites Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!

Reference 7

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verified exact
local_arxiv, observed 2026-08-10T05:55:08.007906Z

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.

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Observation f787c4b1-dd16-4e15-9ebe-0f41ce72a444 · outbound

This paper cites We usually don’t like going to the dentist: Using common sense to detect irony on twitter,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning We usually don’t like going to the dentist: Using common sense to detect irony on twitter,

Reference 8

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raw_fallback, observed 2026-08-10T05:55:08.380672Z

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.

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Observation 19844420-f2a4-4b68-b1a9-71fc48ad877b · outbound

This paper cites Semeval-2022 task 6: isarcasmeval, intended sarcasm detection in english and arabic,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Semeval-2022 task 6: isarcasmeval, intended sarcasm detection in english and arabic,

Reference 9

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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.

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Observation e5314325-a1a9-4766-88c4-efbf85a06441 · outbound

This paper cites Semeval-2018 task 3: Irony detection in english tweets,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Semeval-2018 task 3: Irony detection in english tweets,

Reference 10

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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.

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Observation 05320eb1-9a86-4190-8660-e8c2eb1f6227 · outbound

This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Harnessing the power of llms in practice: A survey on chatgpt and beyond,

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.513747Z digest=sha256:4bfc2e801d47d4be41e53db1e97f12906b0fd1ac42896c8fc3d7be1734f70d4a

Observation 9f208e03-4b37-419a-a177-da1ae4b632b4 · outbound

This paper cites Language Models are Few-Shot Learners.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Language Models are Few-Shot Learners

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.517685Z digest=sha256:541587d30738e2cb83517232e64acf187c397dc1e9dd9069b35674a00f3c944d

Observation 83855930-efc6-4bc9-bfa7-aeb88b5c9e0c · outbound

This paper cites ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting.

Irony Detection, Reasoning and Understanding in Zero-shot Learning ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting

Reference 13

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no resolver link, observed 2026-08-10T05:55:07.521853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.521853Z digest=sha256:ae4a9b0fa98a68b94869222ad80a06cc726c9ebb83393015f9ab7897a8d98538

Observation cb6e10f7-0a84-4208-bb62-77fb2d4e9aca · outbound

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

Irony Detection, Reasoning and Understanding in Zero-shot Learning Chain-of-thought prompting elicits reasoning in large language models,

Reference 14

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no resolver link, observed 2026-08-10T05:55:07.526289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.526289Z digest=sha256:7c4b638609fd7b8fff23b89c43375425ce20580293cb9b92e9517ea19cd24253

Observation 9150f0ec-342b-426e-85af-d7dba2f764c0 · outbound

This paper cites Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b78a975e-f980-4879-b10f-abfa70901756 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Training language models to follow instructions with human feedback,

Reference 16

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no resolver link, observed 2026-08-10T05:55:07.534439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 21c73308-35f2-4ff6-8dbb-a759efcb5bbe · outbound

This paper cites Chatgpt: A comprehensive review on background, applica- tions, key challenges, bias, ethics, limitations and future scope,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Chatgpt: A comprehensive review on background, applica- tions, key challenges, bias, ethics, limitations and future scope,

Reference 17

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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.

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Observation b352b043-fb5c-42ef-ae61-725b13b9c53c · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Irony Detection, Reasoning and Understanding in Zero-shot Learning A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c8c9b953-5a1d-4215-80f0-dede6267aa1e · outbound

This paper cites Chatgpt is a remarkable tool—for experts,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Chatgpt is a remarkable tool—for experts,

Reference 19

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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.

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Observation b34d8f39-90d8-4cde-8092-86ab121ad9ac · outbound

This paper cites Parsing-based sarcasm sentiment recognition in twitter data,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Parsing-based sarcasm sentiment recognition in twitter data,

Reference 20

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raw_fallback, observed 2026-08-10T05:55:08.299819Z

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.

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Observation 4d045def-156a-452e-83c0-a84e7762f6eb · outbound

This paper cites Sarcasm as contrast between a positive sentiment and negative situa- tion,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Sarcasm as contrast between a positive sentiment and negative situa- tion,

Reference 21

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raw_fallback, observed 2026-08-10T05:55:08.288230Z

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.

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Observation 5d4bda3c-4e6c-49ff-861e-fdd4d3def8b4 · outbound

This paper cites Using lexical resources for irony and sarcasm classification,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Using lexical resources for irony and sarcasm classification,

Reference 22

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raw_fallback, observed 2026-08-10T05:55:08.276341Z

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.

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Observation 9bc623ee-a470-490f-b5f2-40cbe66e0d7e · outbound

This paper cites Identifying sar- casm in twitter: a closer look,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Identifying sar- casm in twitter: a closer look,

Reference 23

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raw_fallback, observed 2026-08-10T05:55:08.265685Z

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.

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Observation c13bde5e-4485-480b-ae34-8eb489921b13 · outbound

This paper cites Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis,

Reference 24

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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.

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Observation 22b75add-bd06-444f-a2a2-0f03a68188c7 · outbound

This paper cites Sar- casm and irony detection in english tweets,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Sar- casm and irony detection in english tweets,

Reference 25

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raw_fallback, observed 2026-08-10T05:55:08.243066Z

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.

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Observation 4ce411d5-32c0-49f8-868f-c042fa2d9829 · outbound

This paper cites an unresolved cited work.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Unresolved cited work

Reference 26

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unresolved
raw_fallback, observed 2026-08-10T05:55:08.231961Z

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.

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Observation bc4697b8-742b-4cfe-8e7e-058c8981ac1c · outbound

This paper cites Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.221080Z

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.

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Observation dc331c05-d48c-4038-8940-a13bde0a3ba6 · outbound

This paper cites Applying basic fea- tures from sentiment analysis for automatic irony detection,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Applying basic fea- tures from sentiment analysis for automatic irony detection,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.209136Z

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.

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Observation d8771c3b-9bb5-4721-8a68-4da9a762c893 · outbound

This paper cites Towards a contextual pragmatic model to detect irony in tweets,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Towards a contextual pragmatic model to detect irony in tweets,

Reference 29

Resolution
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raw_fallback, observed 2026-08-10T05:55:08.197819Z

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.

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Observation 4add9a18-c099-4e68-9f4c-9b585ed7972f · outbound

This paper cites Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.185848Z

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.

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Observation 5bd84ab6-af61-477b-befe-27e1fa081b3c · outbound

This paper cites Irony detection with atten- tive recurrent neural networks,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Irony detection with atten- tive recurrent neural networks,

Reference 31

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raw_fallback, observed 2026-08-10T05:55:08.174304Z

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.

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Observation 638ce0bb-6cf0-4f62-a889-e0264aa68fcc · outbound

This paper cites Transformer-based word embedding with cnn model to detect sarcasm and irony,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Transformer-based word embedding with cnn model to detect sarcasm and irony,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.162865Z

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.

source=pdf_text observed=2026-08-10T05:55:07.594719Z digest=sha256:54cdff3d0cc12a3e74cb45721b1d1e0c68a887bd7bd50ac22f511639f1fd1406

Observation 8b9b3992-57b5-47a0-98be-c19b24bc492a · outbound

This paper cites Utilizing an attention-based lstm model for detecting sarcasm and irony in social media,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Utilizing an attention-based lstm model for detecting sarcasm and irony in social media,

Reference 33

Resolution
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raw_fallback, observed 2026-08-10T05:55:08.150771Z

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.

source=pdf_text observed=2026-08-10T05:55:07.598415Z digest=sha256:c673199e5188e2917a795c2bcd2dbfab0ca77d1a744e7c50929276f4dc8e537b

Observation 7f26f097-d523-4596-b327-100f37c8b40a · outbound

This paper cites Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-10T05:55:07.946969Z

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.

source=pdf_text observed=2026-08-10T05:55:07.602233Z digest=sha256:5931d7bc5a4ea31279703005d3b4587c309148341d88417b10434b9e36f77f3d

Observation 39e45a7e-c253-41e9-bf67-6d1e4a4a018c · outbound

This paper cites On Sarcasm Detection with OpenAI GPT-based Models.

Irony Detection, Reasoning and Understanding in Zero-shot Learning On Sarcasm Detection with OpenAI GPT-based Models

Reference 35

Resolution
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local_arxiv, observed 2026-08-10T05:55:07.930613Z

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.

source=pdf_text observed=2026-08-10T05:55:07.606075Z digest=sha256:f9671bcf9e109a02af4f7a105dbcb70b7a9687c36cb8046a35cc5eeee8a4cf46

Observation 87233b13-4354-4097-aa08-924d55a93e51 · outbound

This paper cites Generative pre-trained transformer (gpt) models for irony detection and classification,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Generative pre-trained transformer (gpt) models for irony detection and classification,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.138889Z

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.

source=pdf_text observed=2026-08-10T05:55:07.610074Z digest=sha256:a3a93ddc5be266c3e0d5b2d0ab6f991e53c9d6e24293f1472d96e4f03fc6685a

Observation 845fae71-7822-4a2e-a43f-9045d0e359c1 · outbound

This paper cites Improving language understanding by generative pre- training,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Improving language understanding by generative pre- training,

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.613569Z digest=sha256:3736c26c2ed9bb0753f544cee7e43a7466ea1e4c04bf8aef7da49885e57a0c01

Observation 1d7dcfae-b88b-41af-90b9-86c29e91774c · outbound

This paper cites Language models are unsupervised multitask learners,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Language models are unsupervised multitask learners,

Reference 38

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.617178Z digest=sha256:97c05dfbc25515775a5310323413da018ccc62c5848d2ded50576cf0a9880243

Observation 2331ac1b-4e97-4206-b2a1-d8549e5bca80 · outbound

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

Irony Detection, Reasoning and Understanding in Zero-shot Learning Large lan- guage models are zero-shot reasoners,

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.620821Z digest=sha256:01436be42c6043089a0169d6ac340f9743b6f261e098de380725083985c01613

Observation 0edf913c-857a-4206-855d-2084c0cd2fbc · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 40

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no resolver link, observed 2026-08-10T05:55:07.624467Z

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source=pdf_text observed=2026-08-10T05:55:07.624467Z digest=sha256:2c308fbc24f8ca601d79dfe3d0cb48b302df23df748032733c09e94db3c0ea37

Observation 99137402-731d-4b8f-b006-e0e31138f96d · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Automatic Chain of Thought Prompting in Large Language Models

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.628571Z digest=sha256:3251753d81c606913a3106ad68510598d26ba99e8453dc4f5fd70a30501a7069

Observation 756ab575-49f1-4b81-958d-70a662f8c967 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Tree of thoughts: Deliberate problem solving with large language models,

Reference 42

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.632616Z digest=sha256:76ab6faae7f45893db66f55598e65339ee40f905af43e412b4568e33a7e56320

Observation 99eda110-7368-4893-a9fe-f27b3e737372 · outbound

This paper cites Large Language Model Guided Tree-of-Thought.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Large Language Model Guided Tree-of-Thought

Reference 43

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no resolver link, observed 2026-08-10T05:55:07.636158Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.636158Z digest=sha256:339f2a5951fd85d6183c4c9341c0b054bc7b585a12a79f3139de7dcf608b889f

Observation 80468599-b9f4-4952-bb5b-17e773f5735d · outbound

This paper cites Using tree-of-thought prompting to boost chatgpt’s reason- ing,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Using tree-of-thought prompting to boost chatgpt’s reason- ing,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.099029Z

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.

source=pdf_text observed=2026-08-10T05:55:07.640244Z digest=sha256:da00a3ba3520551b5bdd8abf5e1c36879af8ae9ab810b9ad00169199507270d3

Observation a28c786c-7880-430a-bb19-97c18d1abd7e · outbound

This paper cites Generated Knowledge Prompting for Commonsense Reasoning.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Generated Knowledge Prompting for Commonsense Reasoning

Reference 45

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.643865Z digest=sha256:99f661027c0a23b965ed7506ad2a8c10d6d08d0fcf3b62e9ce0b538e6f661637

Observation fa3cc22f-00c9-403a-a088-893b9bd3e3b9 · outbound

This paper cites Large language models as optimizers,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Large language models as optimizers,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.085941Z

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.

source=pdf_text observed=2026-08-10T05:55:07.647795Z digest=sha256:f14b1057a29a5392f8765018f227dc2feaaef27696063c16887322e26ca6548a

Observation f0eb2fe5-b2b8-4cb4-a628-bc46b236e57d · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 47

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no resolver link, observed 2026-08-10T05:55:07.651619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.651619Z digest=sha256:783fc3aec9533960a8165430db584cce0e7a3a4d368f35d1043ae816acfe6bdb

Observation 21d72cdb-8814-4217-a092-a005c209c060 · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Large Language Models Are Human-Level Prompt Engineers

Reference 48

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.655695Z digest=sha256:e219c98bae05d474df12eb74cc5a7f126cec4beeeb325189f661d5e5a4ddca37

Observation e6b75840-1e0c-42c5-bb32-0ecc2eeec120 · outbound

This paper cites PRewrite: Prompt Rewriting with Reinforcement Learning.

Irony Detection, Reasoning and Understanding in Zero-shot Learning PRewrite: Prompt Rewriting with Reinforcement Learning

Reference 49

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.659562Z digest=sha256:5d1b72028e18cacad6ff916f74b311a56a27006d5be7be2399848357d354bc09

Observation 2046e478-c791-4813-b3fb-813645a68a01 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Finetuned Language Models Are Zero-Shot Learners

Reference 51

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.666904Z digest=sha256:40b8b5c557bda63b309ec88fa1daba7e919caa39929b48910428aa6b689ffe6f

Observation 91bf4cd0-142c-4112-a371-62751287f246 · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 52

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no resolver link, observed 2026-08-10T05:55:07.671303Z

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source=pdf_text observed=2026-08-10T05:55:07.671303Z digest=sha256:35dbda21452e8a28b1622be620bbec0ea431dce3bf0f405ee7d9d8b76cc18143

Observation 5140df99-96d5-424f-8e40-a5ea160dd7c3 · outbound

This paper cites Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue

Reference 53

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T05:55:07.675362Z digest=sha256:39fd2cddbf7384fa9b4e23485b3553532fc9dfbcbddb68d777be3915435a8ab1

Observation 77790b53-1af1-4067-a6f1-7292897387b2 · outbound

This paper cites Humans require context to infer ironic intent (so computers probably do, too),.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Humans require context to infer ironic intent (so computers probably do, too),

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.074284Z

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.

source=pdf_text observed=2026-08-10T05:55:07.679056Z digest=sha256:5b622f6b888d56adccc8922e3d7131fcef3c64b970909764c138642d2331343c

Observation 6665142e-d789-479e-835a-ed20dbcf45b0 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Irony Detection, Reasoning and Understanding in Zero-shot Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 55

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source=pdf_text observed=2026-08-10T05:55:07.682538Z digest=sha256:d3e0e458f968c9cd2e0a9ad39ddb203c60160138438976306c4e85bbb1e1ce9c

Observation 82d49f08-8c0e-4091-b681-04696323a3a1 · outbound

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

Irony Detection, Reasoning and Understanding in Zero-shot Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 56

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source=pdf_text observed=2026-08-10T05:55:07.686372Z digest=sha256:f720b427bb628b94695a6d6da080412ebdff4bb9602999110cfc1d2b3a351bc0

Observation e45f9d29-cca9-46f4-ae57-b517383ce9aa · outbound

This paper cites Mpnet: Masked and permuted pre-training for language understanding,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Mpnet: Masked and permuted pre-training for language understanding,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.062478Z

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.

source=pdf_text observed=2026-08-10T05:55:07.690190Z digest=sha256:db1fc2b6646f0fea36cb654807c385d799fc1b355844ec10b0d9065b9fbfff7c

Observation 350db769-5845-4824-96ef-e501f1b87ba0 · outbound

This paper cites A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives.

Irony Detection, Reasoning and Understanding in Zero-shot Learning A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-10T05:55:07.768263Z

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.

source=pdf_text observed=2026-08-10T05:55:07.693731Z digest=sha256:0febc0b6ab3eb2cbdfdd53449c4a5623c764c71b478a5cb4152565cc1a61eb5a

Observation 3fca4c73-fc30-4c9c-bc8f-76a91cdf21b2 · outbound

This paper cites Shalev-Shwartz and S.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Shalev-Shwartz and S

Reference 59

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.697683Z digest=sha256:80e9c9dd5157b80cf153f5b72a13d7d9a6e4b3a90afba0d03099cfdf645652f4

Observation d59be93c-fc03-4da8-bc6a-cc250b83107c · outbound

This paper cites Machine reasoning: Technology, dilemma and future,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Machine reasoning: Technology, dilemma and future,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.041216Z

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.

source=pdf_text observed=2026-08-10T05:55:07.701942Z digest=sha256:89c5cb957324030ade0962b9ac4be08fe6342f9ab029bcde65971aef8f9b46d7

Observation 9d738570-31ff-4932-a25c-f6df88209306 · outbound

This paper cites Studying irony detection beyond ironic criticism: Let’s include ironic praise,.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Studying irony detection beyond ironic criticism: Let’s include ironic praise,

Reference 61

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verified fuzzy
raw_fallback, observed 2026-08-10T05:55:08.029023Z

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.

source=pdf_text observed=2026-08-10T05:55:07.705829Z digest=sha256:19f4d792269fc1c570c98a15bdfea424441aacf1f2793455079ff5096603a03c

Observation ece74cf4-2100-47cb-bfd8-4a82eceb662f · outbound

This paper cites Meta Prompting for AI Systems.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Meta Prompting for AI Systems

Reference 62

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.709990Z digest=sha256:f93c742efae00b2a937485425e3d38f58e96aacdfdc72e55564d1c03e6ebaeb4

Observation a2575f4f-e191-4276-8482-cff25a8611c8 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 63

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no resolver link, observed 2026-08-10T05:55:07.714092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.714092Z digest=sha256:212cb04c8cca26cdea25703d3027ac91d69c7ad54b0a8e04a8ad968da3a7b36c

Observation 9c00aa1b-1a33-42a0-9cac-cc1dde692a4f · outbound

This paper cites Simplification of flesch reading ease formula.

Irony Detection, Reasoning and Understanding in Zero-shot Learning Simplification of flesch reading ease formula

Reference 64

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no resolver link, observed 2026-08-10T05:55:07.718017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:55:07.718017Z digest=sha256:4224573c9a313d2fbbf7e689f9465a607bb921f8c7f373a4828d0b80ee0ca4c0

Pith citing papers

No inbound Pith citation observations are available.