{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FYQ4JMVPLZAA7WWMFCUWER5L5P","short_pith_number":"pith:FYQ4JMVP","schema_version":"1.0","canonical_sha256":"2e21c4b2af5e400fdacc28a96247abebfbdb4ea9a4e6627d7412db407655aba3","source":{"kind":"arxiv","id":"2409.00222","version":7},"attestation_state":"computed","paper":{"title":"Can Large Language Models Address Open-Target Stance Detection?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abu Ubaida Akash, Ahmed Fahmy, Amine Trabelsi","submitted_at":"2024-08-30T19:26:15Z","abstract_excerpt":"Stance detection (SD) identifies the text position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) from GPT, Gemini, Llama, and Mistral families, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We als"},"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":"2409.00222","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-30T19:26:15Z","cross_cats_sorted":[],"title_canon_sha256":"04c127c69bcfa8e8a3d627023b2e3c133e15345492a5ad3aa4c0a89838c85c02","abstract_canon_sha256":"cda3b37b081f9f05be531f93d5d255548bbbfa90a8181eb4516ce0e9ee8706a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:19.875801Z","signature_b64":"16X2aitlZ2zmEO43Yrn8zhztVzq/5CENtT53ThwjV0/ksB7hCnnH6v99EaTNfiJ2tuMqtxlCtQ+VeM5yZjV/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e21c4b2af5e400fdacc28a96247abebfbdb4ea9a4e6627d7412db407655aba3","last_reissued_at":"2026-07-05T11:12:19.875218Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:19.875218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Large Language Models Address Open-Target Stance Detection?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abu Ubaida Akash, Ahmed Fahmy, Amine Trabelsi","submitted_at":"2024-08-30T19:26:15Z","abstract_excerpt":"Stance detection (SD) identifies the text position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) from GPT, Gemini, Llama, and Mistral families, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00222","kind":"arxiv","version":7},"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/2409.00222/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":"2409.00222","created_at":"2026-07-05T11:12:19.875296+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.00222v7","created_at":"2026-07-05T11:12:19.875296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00222","created_at":"2026-07-05T11:12:19.875296+00:00"},{"alias_kind":"pith_short_12","alias_value":"FYQ4JMVPLZAA","created_at":"2026-07-05T11:12:19.875296+00:00"},{"alias_kind":"pith_short_16","alias_value":"FYQ4JMVPLZAA7WWM","created_at":"2026-07-05T11:12:19.875296+00:00"},{"alias_kind":"pith_short_8","alias_value":"FYQ4JMVP","created_at":"2026-07-05T11:12:19.875296+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08440","citing_title":"Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P","json":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P.json","graph_json":"https://pith.science/api/pith-number/FYQ4JMVPLZAA7WWMFCUWER5L5P/graph.json","events_json":"https://pith.science/api/pith-number/FYQ4JMVPLZAA7WWMFCUWER5L5P/events.json","paper":"https://pith.science/paper/FYQ4JMVP"},"agent_actions":{"view_html":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P","download_json":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P.json","view_paper":"https://pith.science/paper/FYQ4JMVP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.00222&json=true","fetch_graph":"https://pith.science/api/pith-number/FYQ4JMVPLZAA7WWMFCUWER5L5P/graph.json","fetch_events":"https://pith.science/api/pith-number/FYQ4JMVPLZAA7WWMFCUWER5L5P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P/action/storage_attestation","attest_author":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P/action/author_attestation","sign_citation":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P/action/citation_signature","submit_replication":"https://pith.science/pith/FYQ4JMVPLZAA7WWMFCUWER5L5P/action/replication_record"}},"created_at":"2026-07-05T11:12:19.875296+00:00","updated_at":"2026-07-05T11:12:19.875296+00:00"}