{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OWILQUBL3MGZMLQWG65WXGHLMR","short_pith_number":"pith:OWILQUBL","schema_version":"1.0","canonical_sha256":"7590b8502bdb0d962e1637bb6b98eb646f57cdaed44f3fb06221048827e41f94","source":{"kind":"arxiv","id":"2310.14478","version":1},"attestation_state":"computed","paper":{"title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Muhao Chen, Wenxuan Zhou, Yao-Yi Chiang, Zekun Li","submitted_at":"2023-10-23T01:20:01Z","abstract_excerpt":"Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as "},"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":"2310.14478","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T01:20:01Z","cross_cats_sorted":[],"title_canon_sha256":"8521b8cb1bf8f68ed1e4a8e047ee22deb2b4aac61cf5cdd3ad7628142e02ae17","abstract_canon_sha256":"8b482e3f7d3cc050b07643f63ccf5c60d8e0d5c27d66259e299cd4404234a9c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:03:44.837276Z","signature_b64":"vRVP6CQzPIcu3KoyWQVmb1WirPcT1/StgQH12cFY6ra9qbFZMc3WsAPjqZqJYX1H95WE/mMMtebyvBBpevgcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7590b8502bdb0d962e1637bb6b98eb646f57cdaed44f3fb06221048827e41f94","last_reissued_at":"2026-07-05T07:03:44.836881Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:03:44.836881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Muhao Chen, Wenxuan Zhou, Yao-Yi Chiang, Zekun Li","submitted_at":"2023-10-23T01:20:01Z","abstract_excerpt":"Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14478","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/2310.14478/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":"2310.14478","created_at":"2026-07-05T07:03:44.836940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.14478v1","created_at":"2026-07-05T07:03:44.836940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14478","created_at":"2026-07-05T07:03:44.836940+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWILQUBL3MGZ","created_at":"2026-07-05T07:03:44.836940+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWILQUBL3MGZMLQW","created_at":"2026-07-05T07:03:44.836940+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWILQUBL","created_at":"2026-07-05T07:03:44.836940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.16006","citing_title":"DIGMAPPER: A Modular System for Automated Geologic Map Digitization","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR","json":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR.json","graph_json":"https://pith.science/api/pith-number/OWILQUBL3MGZMLQWG65WXGHLMR/graph.json","events_json":"https://pith.science/api/pith-number/OWILQUBL3MGZMLQWG65WXGHLMR/events.json","paper":"https://pith.science/paper/OWILQUBL"},"agent_actions":{"view_html":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR","download_json":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR.json","view_paper":"https://pith.science/paper/OWILQUBL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.14478&json=true","fetch_graph":"https://pith.science/api/pith-number/OWILQUBL3MGZMLQWG65WXGHLMR/graph.json","fetch_events":"https://pith.science/api/pith-number/OWILQUBL3MGZMLQWG65WXGHLMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR/action/storage_attestation","attest_author":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR/action/author_attestation","sign_citation":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR/action/citation_signature","submit_replication":"https://pith.science/pith/OWILQUBL3MGZMLQWG65WXGHLMR/action/replication_record"}},"created_at":"2026-07-05T07:03:44.836940+00:00","updated_at":"2026-07-05T07:03:44.836940+00:00"}