{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MJSZGQI7B5NFVDZICYOLDQ72AG","short_pith_number":"pith:MJSZGQI7","schema_version":"1.0","canonical_sha256":"626593411f0f5a5a8f28161cb1c3fa01bbd3342e01e148833824e5e495e8f6e9","source":{"kind":"arxiv","id":"2412.07906","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Emotion Annotations in the Era of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amrit Romana, Emily Mower Provost, Minxue Niu, Yara El-Tawil","submitted_at":"2024-12-10T20:30:51Z","abstract_excerpt":"Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, sensitive to study design, and difficult to quality control, because of the subjective nature of emotions. Meanwhile, Large Language Models (LLMs) have shown remarkable performance on many Natural Language Understanding tasks, emerging as a promising tool for text annotation. In this work, we analyze the complexities of emotion annotation in the context of LLMs, focusing on GPT-4 as a leading model. In our experiments,"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.07906","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-10T20:30:51Z","cross_cats_sorted":[],"title_canon_sha256":"920404cd1dddef8517178198abd1ff66f16da293ea8409dc12dfd2e74c36baab","abstract_canon_sha256":"434aa7fb25ceac0b501821ba57f71ddfcb74764c1a0d020fccc961747c34d618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:41.019745Z","signature_b64":"Vn6x7CFMpCEPuWR7G83i5YQ9pQXHahYKqldHLIVNKegSWTqAlY+g5rCOKbMYi8v/PLxsbLPUmhigWir6gCXpDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"626593411f0f5a5a8f28161cb1c3fa01bbd3342e01e148833824e5e495e8f6e9","last_reissued_at":"2026-07-05T09:47:41.019197Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:41.019197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Emotion Annotations in the Era of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amrit Romana, Emily Mower Provost, Minxue Niu, Yara El-Tawil","submitted_at":"2024-12-10T20:30:51Z","abstract_excerpt":"Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, sensitive to study design, and difficult to quality control, because of the subjective nature of emotions. Meanwhile, Large Language Models (LLMs) have shown remarkable performance on many Natural Language Understanding tasks, emerging as a promising tool for text annotation. In this work, we analyze the complexities of emotion annotation in the context of LLMs, focusing on GPT-4 as a leading model. In our experiments,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07906","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.07906/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.07906","created_at":"2026-07-05T09:47:41.019249+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.07906v1","created_at":"2026-07-05T09:47:41.019249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07906","created_at":"2026-07-05T09:47:41.019249+00:00"},{"alias_kind":"pith_short_12","alias_value":"MJSZGQI7B5NF","created_at":"2026-07-05T09:47:41.019249+00:00"},{"alias_kind":"pith_short_16","alias_value":"MJSZGQI7B5NFVDZI","created_at":"2026-07-05T09:47:41.019249+00:00"},{"alias_kind":"pith_short_8","alias_value":"MJSZGQI7","created_at":"2026-07-05T09:47:41.019249+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG","json":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG.json","graph_json":"https://pith.science/api/pith-number/MJSZGQI7B5NFVDZICYOLDQ72AG/graph.json","events_json":"https://pith.science/api/pith-number/MJSZGQI7B5NFVDZICYOLDQ72AG/events.json","paper":"https://pith.science/paper/MJSZGQI7"},"agent_actions":{"view_html":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG","download_json":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG.json","view_paper":"https://pith.science/paper/MJSZGQI7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.07906&json=true","fetch_graph":"https://pith.science/api/pith-number/MJSZGQI7B5NFVDZICYOLDQ72AG/graph.json","fetch_events":"https://pith.science/api/pith-number/MJSZGQI7B5NFVDZICYOLDQ72AG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG/action/storage_attestation","attest_author":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG/action/author_attestation","sign_citation":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG/action/citation_signature","submit_replication":"https://pith.science/pith/MJSZGQI7B5NFVDZICYOLDQ72AG/action/replication_record"}},"created_at":"2026-07-05T09:47:41.019249+00:00","updated_at":"2026-07-05T09:47:41.019249+00:00"}