{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EL73KOFOVT5442RS2O2B2MN3RJ","short_pith_number":"pith:EL73KOFO","schema_version":"1.0","canonical_sha256":"22ffb538aeacfbce6a32d3b41d31bb8a700af911f7c8a51c1f5ba45fa96f461e","source":{"kind":"arxiv","id":"2412.03343","version":1},"attestation_state":"computed","paper":{"title":"Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Julie Carson-Berndsen, Long Mai","submitted_at":"2024-12-04T14:23:16Z","abstract_excerpt":"While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. Although several fine-tuning and prompting techniques have been suggested to tackle the issue, they are often tailored to specific tasks or come with a substantial increase in computational cost and latency. This makes them challenging to apply to applications that demand very low"},"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":"2412.03343","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T14:23:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0401afc6ef7be281af7c150aaae70f84939c7389674e731612d7943e42b31a4f","abstract_canon_sha256":"596d49c2f15f65c9713192e819e3415431a4c64200beae755d4b2838c8291b90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:28.172156Z","signature_b64":"n+ZGY3cC7aLz6KN6BghZ7tQuTGT7tBE9VwTEreTpewOpwHNG7aUVh28Z3d29G0yZwo1eLcbgRrVF+pkQrT3PDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22ffb538aeacfbce6a32d3b41d31bb8a700af911f7c8a51c1f5ba45fa96f461e","last_reissued_at":"2026-07-05T09:44:28.171754Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:28.171754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Julie Carson-Berndsen, Long Mai","submitted_at":"2024-12-04T14:23:16Z","abstract_excerpt":"While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. Although several fine-tuning and prompting techniques have been suggested to tackle the issue, they are often tailored to specific tasks or come with a substantial increase in computational cost and latency. This makes them challenging to apply to applications that demand very low"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03343","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/2412.03343/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":"2412.03343","created_at":"2026-07-05T09:44:28.171813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.03343v1","created_at":"2026-07-05T09:44:28.171813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03343","created_at":"2026-07-05T09:44:28.171813+00:00"},{"alias_kind":"pith_short_12","alias_value":"EL73KOFOVT54","created_at":"2026-07-05T09:44:28.171813+00:00"},{"alias_kind":"pith_short_16","alias_value":"EL73KOFOVT5442RS","created_at":"2026-07-05T09:44:28.171813+00:00"},{"alias_kind":"pith_short_8","alias_value":"EL73KOFO","created_at":"2026-07-05T09:44:28.171813+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18739","citing_title":"Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ","json":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ.json","graph_json":"https://pith.science/api/pith-number/EL73KOFOVT5442RS2O2B2MN3RJ/graph.json","events_json":"https://pith.science/api/pith-number/EL73KOFOVT5442RS2O2B2MN3RJ/events.json","paper":"https://pith.science/paper/EL73KOFO"},"agent_actions":{"view_html":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ","download_json":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ.json","view_paper":"https://pith.science/paper/EL73KOFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.03343&json=true","fetch_graph":"https://pith.science/api/pith-number/EL73KOFOVT5442RS2O2B2MN3RJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EL73KOFOVT5442RS2O2B2MN3RJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ/action/storage_attestation","attest_author":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ/action/author_attestation","sign_citation":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ/action/citation_signature","submit_replication":"https://pith.science/pith/EL73KOFOVT5442RS2O2B2MN3RJ/action/replication_record"}},"created_at":"2026-07-05T09:44:28.171813+00:00","updated_at":"2026-07-05T09:44:28.171813+00:00"}