{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JHAFFCFTAYE5RY765TKISCZONI","short_pith_number":"pith:JHAFFCFT","schema_version":"1.0","canonical_sha256":"49c05288b30609d8e3feecd4890b2e6a05fd511dd5f337c52820d0b0d9ddd644","source":{"kind":"arxiv","id":"2407.11638","version":2},"attestation_state":"computed","paper":{"title":"A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Chenchen Ye, He Chang, Jie Wu, Tat-Seng Chua, Xianglin Huang, Yunshan Ma, Zhengmao Yang, Zhulin Tao","submitted_at":"2024-07-16T11:58:54Z","abstract_excerpt":"Recently, Large Language Models (LLMs) have demonstrated great potential in various data mining tasks, such as knowledge question answering, mathematical reasoning, and commonsense reasoning. However, the reasoning capability of LLMs on temporal event forecasting has been under-explored. To systematically investigate their abilities in temporal event forecasting, we conduct a comprehensive evaluation of LLM-based methods for temporal event forecasting. Due to the lack of a high-quality dataset that involves both graph and textual data, we first construct a benchmark dataset, named MidEast-TE-m"},"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":"2407.11638","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-16T11:58:54Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"daaa8ffd644b3b092967b2fa7a66c0917821ee0549340863e14cd4c3d6c48054","abstract_canon_sha256":"67c3417fd1f3ec8eedd2a75362f1c27c2c0ce992d4d81fce68bb728cfa0b2032"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:06:22.436197Z","signature_b64":"6LR3+I1eaM5OEJHfa9zktiZIM5stffcBPMe2+a1Zpzc7FeT+gJBkVnn3bmz6AkNIpO7hItYXpCkbvSSfl/aZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49c05288b30609d8e3feecd4890b2e6a05fd511dd5f337c52820d0b0d9ddd644","last_reissued_at":"2026-07-05T11:06:22.435695Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:06:22.435695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Chenchen Ye, He Chang, Jie Wu, Tat-Seng Chua, Xianglin Huang, Yunshan Ma, Zhengmao Yang, Zhulin Tao","submitted_at":"2024-07-16T11:58:54Z","abstract_excerpt":"Recently, Large Language Models (LLMs) have demonstrated great potential in various data mining tasks, such as knowledge question answering, mathematical reasoning, and commonsense reasoning. However, the reasoning capability of LLMs on temporal event forecasting has been under-explored. To systematically investigate their abilities in temporal event forecasting, we conduct a comprehensive evaluation of LLM-based methods for temporal event forecasting. Due to the lack of a high-quality dataset that involves both graph and textual data, we first construct a benchmark dataset, named MidEast-TE-m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11638","kind":"arxiv","version":2},"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/2407.11638/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":"2407.11638","created_at":"2026-07-05T11:06:22.435750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11638v2","created_at":"2026-07-05T11:06:22.435750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11638","created_at":"2026-07-05T11:06:22.435750+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHAFFCFTAYE5","created_at":"2026-07-05T11:06:22.435750+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHAFFCFTAYE5RY76","created_at":"2026-07-05T11:06:22.435750+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHAFFCFT","created_at":"2026-07-05T11:06:22.435750+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31665","citing_title":"ForecastAgentSearch: Towards a Multi-Expert Agent Search System for Geopolitical Event Forecasting","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27881","citing_title":"A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI","json":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI.json","graph_json":"https://pith.science/api/pith-number/JHAFFCFTAYE5RY765TKISCZONI/graph.json","events_json":"https://pith.science/api/pith-number/JHAFFCFTAYE5RY765TKISCZONI/events.json","paper":"https://pith.science/paper/JHAFFCFT"},"agent_actions":{"view_html":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI","download_json":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI.json","view_paper":"https://pith.science/paper/JHAFFCFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11638&json=true","fetch_graph":"https://pith.science/api/pith-number/JHAFFCFTAYE5RY765TKISCZONI/graph.json","fetch_events":"https://pith.science/api/pith-number/JHAFFCFTAYE5RY765TKISCZONI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI/action/storage_attestation","attest_author":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI/action/author_attestation","sign_citation":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI/action/citation_signature","submit_replication":"https://pith.science/pith/JHAFFCFTAYE5RY765TKISCZONI/action/replication_record"}},"created_at":"2026-07-05T11:06:22.435750+00:00","updated_at":"2026-07-05T11:06:22.435750+00:00"}