Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages | Paper Citation Record

Observation bedfef71-2af4-419a-a516-e2f004cff83c · outbound

This paper cites Available: https://www.un.org/en/hate-speech/ understanding-hate-speech/what-is-hate-speech#:~:text=To% 20provide%20a%20unified%20framework,person%20or%20a% 20group%20on.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Available: https://www.un.org/en/hate-speech/ understanding-hate-speech/what-is-hate-speech#:~:text=To% 20provide%20a%20unified%20framework,person%20or%20a% 20group%20on

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.408463Z

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 1d57a065-fd9d-4248-900a-b6a4124866ec · outbound

This paper cites Hate speech review in the context of online social networks,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hate speech review in the context of online social networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.391558Z

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 31202622-76cd-4f56-a2f7-42aefaf6bb16 · outbound

This paper cites Hossain, Oct 2019.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hossain, Oct 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.376160Z

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 15124a3c-591a-455e-ac40-4d2ea9761958 · outbound

This paper cites Indian mob kills man over beef eating rumour,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Indian mob kills man over beef eating rumour,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.361297Z

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 bccf3d74-ff5f-4b2f-9fd1-13658dfe14c4 · outbound

This paper cites Business reputation and social media: A primer on threats and responses,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Business reputation and social media: A primer on threats and responses,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.331258Z

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 dc2d9831-fa31-443e-a1af-9ec4bbdb2dc9 · outbound

This paper cites Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:37.074157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae06e614-4630-49a0-b321-0047ffc28c65 · outbound

This paper cites Prompt learning for low-resource multi-domain fake news detection,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Prompt learning for low-resource multi-domain fake news detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.316484Z

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 7f29d03e-3c4c-4534-a49f-0ce7058b209d · outbound

This paper cites Generating monolingual dataset for low resource language bodo from old books using google keep,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Generating monolingual dataset for low resource language bodo from old books using google keep,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.300299Z

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 fdd5fd11-d314-4e1a-9859-932b40dd2f48 · outbound

This paper cites Prompt-based for low-resource tibetan text classification,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Prompt-based for low-resource tibetan text classification,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.284089Z

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 60743ccb-919d-40a3-93bc-19721597f4eb · outbound

This paper cites Hate Speech and Offensive Language Detection in Bengali.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hate Speech and Offensive Language Detection in Bengali

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:37.448708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4b273cb-e4e4-43bd-9412-e237a99a8872 · outbound

This paper cites Tinyllm efficacy in low-resource language: An experiment on bangla text classification task,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Tinyllm efficacy in low-resource language: An experiment on bangla text classification task,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.268717Z

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 74f0596b-9ce2-4225-8da3-150accbfabe7 · outbound

This paper cites Using a semi- automatic keyword dictionary for improving violent web site filtering,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Using a semi- automatic keyword dictionary for improving violent web site filtering,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.252236Z

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 9d811121-b96e-4979-a3da-c326428677ec · outbound

This paper cites Us and them: identifying cyber hate on twitter across multiple protected characteristics,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Us and them: identifying cyber hate on twitter across multiple protected characteristics,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.237652Z

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 c9ae4cda-ee76-4616-bf26-fcf1f7729d59 · outbound

This paper cites A Dictionary-based Approach to Racism Detection in Dutch Social Media.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A Dictionary-based Approach to Racism Detection in Dutch Social Media

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:37.855433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e5b2b9-63c8-49ec-b7f3-e33f8eb7e19c · outbound

This paper cites A lexicon-based approach for hate speech detection,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A lexicon-based approach for hate speech detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.221834Z

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 f25f97e8-67ff-4979-871c-48de75ec7dd3 · outbound

This paper cites Hate speech detection: Challenges and solutions,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hate speech detection: Challenges and solutions,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.206777Z

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 93004623-7417-4a45-9f35-24d31a2a3dac · outbound

This paper cites A Web of Hate: Tackling Hateful Speech in Online Social Spaces.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A Web of Hate: Tackling Hateful Speech in Online Social Spaces

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:38.070126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a5f98aa-5d4e-4685-9c72-9655f39d7f66 · outbound

This paper cites Detection of hate speech by employing support vector machine with word2vec model,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Detection of hate speech by employing support vector machine with word2vec model,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.192771Z

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 b70a75f8-dfd3-4524-a3b4-7bc5ea762e9b · outbound

This paper cites Hateful symbols or hateful people? predictive features for hate speech detection on twitter,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hateful symbols or hateful people? predictive features for hate speech detection on twitter,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:38.228587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86def259-a6fa-4e85-b75e-dd91bf63f7ab · outbound

This paper cites Svm for hate speech and offensive content detection.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Svm for hate speech and offensive content detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.169051Z

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 5d68d925-2b5e-4744-b9bb-0ebf73a7b2d8 · outbound

This paper cites A comparison of event models for naive bayes text classification,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A comparison of event models for naive bayes text classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.155437Z

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 be4940ad-a1b4-4891-9b96-beafab5e0434 · outbound

This paper cites A survey on hate speech detection and sentiment analysis using machine learning and deep learning models,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A survey on hate speech detection and sentiment analysis using machine learning and deep learning models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.139997Z

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 3d99c5fb-0d36-4153-b3fc-91408e43cd4e · outbound

This paper cites Im- proving random forest method to detect hatespeech and offensive word,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Im- proving random forest method to detect hatespeech and offensive word,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.125033Z

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 b54a1a06-7b63-454b-bf5e-466025a928ab · outbound

This paper cites But i did not mean it!—intent classification of racist posts on tumblr,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages But i did not mean it!—intent classification of racist posts on tumblr,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.110976Z

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 1ebc768d-c13f-4c5a-8c9a-314b4abe3fc0 · outbound

This paper cites Decision trees and random forests: Machine learning techniques to classify rare events,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Decision trees and random forests: Machine learning techniques to classify rare events,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.095396Z

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 686bb51b-03bb-47a8-9b91-46bff0798005 · outbound

This paper cites Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.081704Z

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 165890e7-d2a8-4da0-b660-fd38d0b80cc3 · outbound

This paper cites Using convolutional neural networks to classify hate-speech,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Using convolutional neural networks to classify hate-speech,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.067928Z

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 56058f5e-28d2-422c-9942-62ae89621f83 · outbound

This paper cites QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:43.310225Z

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 a9dccea8-ab23-4d78-a331-982f23422afc · outbound

This paper cites Hate speech detection using attention-based lstm,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hate speech detection using attention-based lstm,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.053803Z

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 45b3a008-e0f4-4cf8-bacb-d48350ab143d · outbound

This paper cites Detection of hate speech and offensive language in twitter data using lstm model,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Detection of hate speech and offensive language in twitter data using lstm model,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.040113Z

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 7facfda6-366f-4112-bf60-bd6eeeea8412 · outbound

This paper cites Deep learning for hate speech detection in tweets,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Deep learning for hate speech detection in tweets,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.025219Z

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 be987b15-b26d-474e-ad0b-82138eb6da71 · outbound

This paper cites A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:39.227664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c0342ba-96d3-4682-b533-82a72dc7fad4 · outbound

This paper cites Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:43.058076Z

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 b14625b6-8bce-4ea8-a972-2cfa29434d39 · outbound

This paper cites Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:39.370338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c5779b4-2c49-44e3-84ce-2303002e17f5 · outbound

This paper cites Many-shot jailbreaking,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Many-shot jailbreaking,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.009945Z

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 be2bf6df-f551-4dd7-a165-c585fd4fbe52 · outbound

This paper cites A Survey on In-context Learning.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A Survey on In-context Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:39.539189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a57cdfbd-c276-4dde-ab81-0386013695d7 · outbound

This paper cites Better Zero-Shot Reasoning with Role-Play Prompting.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Better Zero-Shot Reasoning with Role-Play Prompting

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:39.594919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7b01fa9-4ba7-4c80-a0c6-36dd698722f2 · outbound

This paper cites Respectful or toxic? using zero-shot learning with language models to detect hate speech,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Respectful or toxic? using zero-shot learning with language models to detect hate speech,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.995477Z

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 cd1b96c0-77c5-48d4-96c3-03aac848f324 · outbound

This paper cites Leveraging zero and few-shot learning for enhanced model generality in hate speech detection in spanish and english,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Leveraging zero and few-shot learning for enhanced model generality in hate speech detection in spanish and english,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.839071Z

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 8deb7eab-8437-4511-9d2b-51e039fb0c58 · outbound

This paper cites Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:42.876707Z

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 ac128618-86fd-428c-9b68-3c40acc7e832 · outbound

This paper cites Hypernymy detection for low-resource languages: A study for hindi, bengali, and amharic,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Hypernymy detection for low-resource languages: A study for hindi, bengali, and amharic,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.700761Z

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 f560e2e0-031a-43d6-a0b6-b4e2044aa4c9 · outbound

This paper cites Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:42.697602Z

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 873c0836-8b0e-46bf-807f-531d518b92a5 · outbound

This paper cites A dataset of Hindi-English code-mixed social media text for hate speech detection,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A dataset of Hindi-English code-mixed social media text for hate speech detection,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.540536Z

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 91e61f4a-423d-4e43-b4e1-4f96d100b349 · outbound

This paper cites Navigating linguistic diversity: In-context learning and prompt engineering for subjectivity analysis in low-resource languages,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Navigating linguistic diversity: In-context learning and prompt engineering for subjectivity analysis in low-resource languages,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.407536Z

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 89011bb4-bfbc-494d-affe-044de4534c7d · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Energy and Policy Considerations for Deep Learning in NLP

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:40.263128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21bfb8ad-9d64-4f92-9111-c70d557374d6 · outbound

This paper cites Towards climate awareness in NLP research,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Towards climate awareness in NLP research,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.250339Z

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 f1d7cce9-fc1f-47b1-9c7a-db6afa2b173e · outbound

This paper cites An energy-based comparative analysis of common approaches to text classification in the Legal domain.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages An energy-based comparative analysis of common approaches to text classification in the Legal domain

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:40.437209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7a94f3e-0f51-4fc6-aed2-e8d98cbdbe81 · outbound

This paper cites BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:40.522421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16b7e5a2-e7c3-43df-b572-569ff5c15924 · outbound

This paper cites A curated dataset for hate speech detection on social media text,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages A curated dataset for hate speech detection on social media text,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.127574Z

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 6463418e-e551-4d00-95ef-31f43fd23aed · outbound

This paper cites Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:40.688200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63b14173-5fb9-4530-b2e7-3af7cb2c8f5e · outbound

This paper cites HateCheckHIn: Evaluating Hindi Hate Speech Detection Models.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages HateCheckHIn: Evaluating Hindi Hate Speech Detection Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:42.417825Z

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 815461da-5b1d-4d71-a117-88a01ae3426a · outbound

This paper cites BNLP: Natural language processing toolkit for Bengali language.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages BNLP: Natural language processing toolkit for Bengali language

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:42.301177Z

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 ea0a5344-2fd7-4d1d-8267-c01841b3f2fb · outbound

This paper cites Computers’ interpre- tations of knowledge representation using pre-conceptual schemas: an approach based on the bert and llama 2-chat models,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Computers’ interpre- tations of knowledge representation using pre-conceptual schemas: an approach based on the bert and llama 2-chat models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:44.000116Z

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 a955ef85-d1a6-4350-a04c-b913f2398b1c · outbound

This paper cites Google translate.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Google translate

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:43.947555Z

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 031b3f91-b603-4a8b-a670-e3f3c077f92e · outbound

This paper cites Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:42.165552Z

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 0b491711-6151-47ab-86ad-d5e42cb95a25 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.145478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 344a3bf3-f203-41e6-bc0b-308794abbcaf · outbound

This paper cites Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:43.819281Z

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 5c3a2e56-b65b-4f2d-b0c0-6ed26fb28929 · outbound

This paper cites Available: https://huggingface.co/meta-llama/ Llama-2-7b-chat-hf.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Available: https://huggingface.co/meta-llama/ Llama-2-7b-chat-hf

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:43.602877Z

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 d7716bd8-2019-4761-8538-4511466cf611 · outbound

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

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Large language models are zero-shot reasoners,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.371716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36a7ccd1-900a-40af-92c3-8adda79cb4ea · outbound

This paper cites Don't Say No: Jailbreaking LLM by Suppressing Refusal.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Don't Say No: Jailbreaking LLM by Suppressing Refusal

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.418171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41ced416-2657-4ece-8c9c-b1bbb4e81b9c · outbound

This paper cites Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.494909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da7c1749-0a11-4001-ad89-35f7bc6e16e3 · outbound

This paper cites Language models are few-shot learners,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Language models are few-shot learners,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.548803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e14ac7-bada-4836-b94c-e790f90a5c83 · outbound

This paper cites Learning from others' mistakes: Avoiding dataset biases without modeling them.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Learning from others' mistakes: Avoiding dataset biases without modeling them

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:34:41.989811Z

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 82edd1f4-63aa-4f5d-b684-a8351c01cf05 · outbound

This paper cites The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.685451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03faa283-0c0a-4144-99fc-b4a400fd64a9 · outbound

This paper cites mlco2/codecarbon: v2.4.1,.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages mlco2/codecarbon: v2.4.1,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:41.770580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab030972-1ab9-486d-8afb-da1fc485656e · outbound

This paper cites Available: https://www.aljazeera.com/news/2015/10/1/ indian-mob-kills-man-over-beef-eating-rumour.

Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages Available: https://www.aljazeera.com/news/2015/10/1/ indian-mob-kills-man-over-beef-eating-rumour

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:34:45.346902Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers