{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QDUJKQB54CPZ22CRYFFJFKTQDF","short_pith_number":"pith:QDUJKQB5","schema_version":"1.0","canonical_sha256":"80e895403de09f9d6851c14a92aa7019752f31ab03858a7393864ced571f5ecf","source":{"kind":"arxiv","id":"2509.09689","version":1},"attestation_state":"computed","paper":{"title":"Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Eshani Agrawal, Himanshu Thakur, Smruthi Mukund","submitted_at":"2025-08-18T22:14:57Z","abstract_excerpt":"A long-standing challenge in developing accurate recommendation models is simulating user behavior, mainly due to the complex and stochastic nature of user interactions. Towards this, one promising line of work has been the use of Large Language Models (LLMs) for simulating user behavior. However, aligning these general-purpose large pre-trained models with user preferences necessitates: (i) effectively and continously parsing large-scale tabular user-item interaction data, (ii) overcoming pre-training-induced inductive biases to accurately learn user specific knowledge, and (iii) achieving th"},"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":"2509.09689","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-08-18T22:14:57Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"e30b8edc1a1ad7e61c74a5dc99212d83fbb68cde4c1e75c989632a01549c554f","abstract_canon_sha256":"d8346328b497942a625713456c8cd6385f3e712b5ae41dec79ca6c25261cc0e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:47.117650Z","signature_b64":"rSK7iWD3zOsukjmWohxJe6XDYQi6yMTavW2ZV4fzCOo4LbwYbBZHGaLpFmn9IQH1NalJxWRdu0SElgng2zpOBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80e895403de09f9d6851c14a92aa7019752f31ab03858a7393864ced571f5ecf","last_reissued_at":"2026-07-05T12:09:47.117091Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:47.117091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Eshani Agrawal, Himanshu Thakur, Smruthi Mukund","submitted_at":"2025-08-18T22:14:57Z","abstract_excerpt":"A long-standing challenge in developing accurate recommendation models is simulating user behavior, mainly due to the complex and stochastic nature of user interactions. Towards this, one promising line of work has been the use of Large Language Models (LLMs) for simulating user behavior. However, aligning these general-purpose large pre-trained models with user preferences necessitates: (i) effectively and continously parsing large-scale tabular user-item interaction data, (ii) overcoming pre-training-induced inductive biases to accurately learn user specific knowledge, and (iii) achieving th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09689","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/2509.09689/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":"2509.09689","created_at":"2026-07-05T12:09:47.117155+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.09689v1","created_at":"2026-07-05T12:09:47.117155+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09689","created_at":"2026-07-05T12:09:47.117155+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDUJKQB54CPZ","created_at":"2026-07-05T12:09:47.117155+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDUJKQB54CPZ22CR","created_at":"2026-07-05T12:09:47.117155+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDUJKQB5","created_at":"2026-07-05T12:09:47.117155+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14097","citing_title":"Real-Time Group Dynamics with LLM Facilitation: Evidence from a Charity Allocation Task","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14097","citing_title":"Real-Time Group Dynamics with LLM Facilitation: Evidence from a Charity Allocation Task","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF","json":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF.json","graph_json":"https://pith.science/api/pith-number/QDUJKQB54CPZ22CRYFFJFKTQDF/graph.json","events_json":"https://pith.science/api/pith-number/QDUJKQB54CPZ22CRYFFJFKTQDF/events.json","paper":"https://pith.science/paper/QDUJKQB5"},"agent_actions":{"view_html":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF","download_json":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF.json","view_paper":"https://pith.science/paper/QDUJKQB5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.09689&json=true","fetch_graph":"https://pith.science/api/pith-number/QDUJKQB54CPZ22CRYFFJFKTQDF/graph.json","fetch_events":"https://pith.science/api/pith-number/QDUJKQB54CPZ22CRYFFJFKTQDF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF/action/storage_attestation","attest_author":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF/action/author_attestation","sign_citation":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF/action/citation_signature","submit_replication":"https://pith.science/pith/QDUJKQB54CPZ22CRYFFJFKTQDF/action/replication_record"}},"created_at":"2026-07-05T12:09:47.117155+00:00","updated_at":"2026-07-05T12:09:47.117155+00:00"}