{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EB247F7YW7L7O4FYQFLT3OETS5","short_pith_number":"pith:EB247F7Y","schema_version":"1.0","canonical_sha256":"2075cf97f8b7d7f770b881573db893975dcf5cf3889e607ab86fab6016f1ef16","source":{"kind":"arxiv","id":"2607.28119","version":1},"attestation_state":"computed","paper":{"title":"Challenges in annotations by humans and LLMs: A case study of evaluative language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Aenne Cecilia Kristine Knierim, Ekaterina Lapshinova-Koltunski, Khushi Pitroda, Mirela Imamovic","submitted_at":"2026-07-30T12:28:54Z","abstract_excerpt":"In this paper, we draw a comparison between linguists in training, a trained linguist, and annotations generated by large language models (LLMs) to find out if they struggle with complex linguistic phenomena in a similar way. For this purpose, we analyse evaluative language in spoken popular science discourse, with the example of a corpus of English TED talk transcripts. We focus on the Appraisal theory and its Attitude subsystem, including the categories (classes) of Affect, Judgement, and Appreciation. In this context, Appraisal theory is an example of a highly subjective annotation task, ma"},"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":"2607.28119","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-30T12:28:54Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"720d34a17c280512d2cde7894f163205a452356b1a3e3258424cc2b90366f2f2","abstract_canon_sha256":"30e7d612f480c2d41d65ba261013fe36c635cc17ebb6cea433c0f74751cbac50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2075cf97f8b7d7f770b881573db893975dcf5cf3889e607ab86fab6016f1ef16","last_reissued_at":"2026-07-31T01:35:48.313249Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:35:48.313249Z"},"graph_snapshot":{"paper":{"title":"Challenges in annotations by humans and LLMs: A case study of evaluative language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Aenne Cecilia Kristine Knierim, Ekaterina Lapshinova-Koltunski, Khushi Pitroda, Mirela Imamovic","submitted_at":"2026-07-30T12:28:54Z","abstract_excerpt":"In this paper, we draw a comparison between linguists in training, a trained linguist, and annotations generated by large language models (LLMs) to find out if they struggle with complex linguistic phenomena in a similar way. For this purpose, we analyse evaluative language in spoken popular science discourse, with the example of a corpus of English TED talk transcripts. We focus on the Appraisal theory and its Attitude subsystem, including the categories (classes) of Affect, Judgement, and Appreciation. In this context, Appraisal theory is an example of a highly subjective annotation task, ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28119","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/2607.28119/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":"2607.28119","created_at":"2026-07-31T01:35:48.316539+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28119v1","created_at":"2026-07-31T01:35:48.316539+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28119","created_at":"2026-07-31T01:35:48.316539+00:00"},{"alias_kind":"pith_short_12","alias_value":"EB247F7YW7L7","created_at":"2026-07-31T01:35:48.316539+00:00"},{"alias_kind":"pith_short_16","alias_value":"EB247F7YW7L7O4FY","created_at":"2026-07-31T01:35:48.316539+00:00"},{"alias_kind":"pith_short_8","alias_value":"EB247F7Y","created_at":"2026-07-31T01:35:48.316539+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/EB247F7YW7L7O4FYQFLT3OETS5","json":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5.json","graph_json":"https://pith.science/api/pith-number/EB247F7YW7L7O4FYQFLT3OETS5/graph.json","events_json":"https://pith.science/api/pith-number/EB247F7YW7L7O4FYQFLT3OETS5/events.json","paper":"https://pith.science/paper/EB247F7Y"},"agent_actions":{"view_html":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5","download_json":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5.json","view_paper":"https://pith.science/paper/EB247F7Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28119&json=true","fetch_graph":"https://pith.science/api/pith-number/EB247F7YW7L7O4FYQFLT3OETS5/graph.json","fetch_events":"https://pith.science/api/pith-number/EB247F7YW7L7O4FYQFLT3OETS5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5/action/storage_attestation","attest_author":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5/action/author_attestation","sign_citation":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5/action/citation_signature","submit_replication":"https://pith.science/pith/EB247F7YW7L7O4FYQFLT3OETS5/action/replication_record"}},"created_at":"2026-07-31T01:35:48.316539+00:00","updated_at":"2026-07-31T01:35:48.316539+00:00"}