{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2FL7XURIPIRP2VSCVQVNLDJTHN","short_pith_number":"pith:2FL7XURI","schema_version":"1.0","canonical_sha256":"d157fbd2287a22fd5642ac2ad58d333b56ad1ea1150b1389fd1c7efa71f77abd","source":{"kind":"arxiv","id":"2508.03990","version":1},"attestation_state":"computed","paper":{"title":"Are Today's LLMs Ready to Explain Well-Being Concepts?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Bohan Jiang, Chengshuai Zhao, Dawei Li, Huan Liu, Zhen Tan","submitted_at":"2025-08-06T00:45:02Z","abstract_excerpt":"Well-being encompasses mental, physical, and social dimensions essential to personal growth and informed life decisions. As individuals increasingly consult Large Language Models (LLMs) to understand well-being, a key challenge emerges: Can LLMs generate explanations that are not only accurate but also tailored to diverse audiences? High-quality explanations require both factual correctness and the ability to meet the expectations of users with varying expertise. In this work, we construct a large-scale dataset comprising 43,880 explanations of 2,194 well-being concepts, generated by ten diver"},"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":"2508.03990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-06T00:45:02Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"125e24548a6574c3c489d91b47cea1bdaed5ef83478f5057ed4e590f5f9f15a7","abstract_canon_sha256":"76746238dab4ea989fabdc1bec767b4f8682e962805bd7f83bdc502846cb3cec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:18.558823Z","signature_b64":"O5wwIy3wKWZsNGM0bcbea2toFzAe9v8SySqLOt55CD0sS3GqFzGoJ0oXwm+1WHzCd/r+xwJUh3VGRfbUq+J/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d157fbd2287a22fd5642ac2ad58d333b56ad1ea1150b1389fd1c7efa71f77abd","last_reissued_at":"2026-07-05T11:49:18.558390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:18.558390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are Today's LLMs Ready to Explain Well-Being Concepts?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Bohan Jiang, Chengshuai Zhao, Dawei Li, Huan Liu, Zhen Tan","submitted_at":"2025-08-06T00:45:02Z","abstract_excerpt":"Well-being encompasses mental, physical, and social dimensions essential to personal growth and informed life decisions. As individuals increasingly consult Large Language Models (LLMs) to understand well-being, a key challenge emerges: Can LLMs generate explanations that are not only accurate but also tailored to diverse audiences? High-quality explanations require both factual correctness and the ability to meet the expectations of users with varying expertise. In this work, we construct a large-scale dataset comprising 43,880 explanations of 2,194 well-being concepts, generated by ten diver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03990","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/2508.03990/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":"2508.03990","created_at":"2026-07-05T11:49:18.558447+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.03990v1","created_at":"2026-07-05T11:49:18.558447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03990","created_at":"2026-07-05T11:49:18.558447+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FL7XURIPIRP","created_at":"2026-07-05T11:49:18.558447+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FL7XURIPIRP2VSC","created_at":"2026-07-05T11:49:18.558447+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FL7XURI","created_at":"2026-07-05T11:49:18.558447+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/2FL7XURIPIRP2VSCVQVNLDJTHN","json":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN.json","graph_json":"https://pith.science/api/pith-number/2FL7XURIPIRP2VSCVQVNLDJTHN/graph.json","events_json":"https://pith.science/api/pith-number/2FL7XURIPIRP2VSCVQVNLDJTHN/events.json","paper":"https://pith.science/paper/2FL7XURI"},"agent_actions":{"view_html":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN","download_json":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN.json","view_paper":"https://pith.science/paper/2FL7XURI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.03990&json=true","fetch_graph":"https://pith.science/api/pith-number/2FL7XURIPIRP2VSCVQVNLDJTHN/graph.json","fetch_events":"https://pith.science/api/pith-number/2FL7XURIPIRP2VSCVQVNLDJTHN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN/action/storage_attestation","attest_author":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN/action/author_attestation","sign_citation":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN/action/citation_signature","submit_replication":"https://pith.science/pith/2FL7XURIPIRP2VSCVQVNLDJTHN/action/replication_record"}},"created_at":"2026-07-05T11:49:18.558447+00:00","updated_at":"2026-07-05T11:49:18.558447+00:00"}