{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2E5O47ERTQJTFLZYLIRQNN33UR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"bfe21d5245bf1d5a745ade0fcae22ac2ebd395175ebd5093d40f06cede76b1b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-10T07:13:06Z","title_canon_sha256":"db997946bf9ab142d47e4f24457c3a359ca7c313f5f10167166b5657e90994e2"},"schema_version":"1.0","source":{"id":"2311.05903","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.05903","created_at":"2026-07-05T07:57:46Z"},{"alias_kind":"arxiv_version","alias_value":"2311.05903v2","created_at":"2026-07-05T07:57:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.05903","created_at":"2026-07-05T07:57:46Z"},{"alias_kind":"pith_short_12","alias_value":"2E5O47ERTQJT","created_at":"2026-07-05T07:57:46Z"},{"alias_kind":"pith_short_16","alias_value":"2E5O47ERTQJTFLZY","created_at":"2026-07-05T07:57:46Z"},{"alias_kind":"pith_short_8","alias_value":"2E5O47ER","created_at":"2026-07-05T07:57:46Z"}],"graph_snapshots":[{"event_id":"sha256:621d70d5ec276097e6f6ad71fc9267740846b0a5228f9ac185deea56a393aa75","target":"graph","created_at":"2026-07-05T07:57:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.05903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-technical users. In this paper we tested an unmodified version of GPT 3.5, a fine-tuned version, and the same unmodified model when given access to a vectorised RAG database, both in isolation and in combination with a basic, non-algorithmic soft prompt. In each case we tested the ","authors_text":"Akira Rafhael Janson Pattirane, Alfath Daryl Alhajir, Eko Ridho Dinarto, Jennifer Dodgson, Joseph Lim, Julian Peh, Lin Nanzheng, Syed Danyal Ahmad","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-10T07:13:06Z","title":"Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.05903","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4fad5c62a1339ede7f4f1350094d49a781a3c2e9b1305c62c5ffd98f25705133","target":"record","created_at":"2026-07-05T07:57:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"bfe21d5245bf1d5a745ade0fcae22ac2ebd395175ebd5093d40f06cede76b1b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-10T07:13:06Z","title_canon_sha256":"db997946bf9ab142d47e4f24457c3a359ca7c313f5f10167166b5657e90994e2"},"schema_version":"1.0","source":{"id":"2311.05903","kind":"arxiv","version":2}},"canonical_sha256":"d13aee7c919c1332af385a2306b77ba47a5c0e3482cf11e8d34fac8ae6f212a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d13aee7c919c1332af385a2306b77ba47a5c0e3482cf11e8d34fac8ae6f212a4","first_computed_at":"2026-07-05T07:57:46.122998Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:57:46.122998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B066T7z5L2ye3Ay3Gxc458Tt+e5amfzZGgdYD1wR0RLUu8AyF367uUmMX7ewzn7JsWsI9PDnBrcAXWIQbqbFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:57:46.123546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.05903","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fad5c62a1339ede7f4f1350094d49a781a3c2e9b1305c62c5ffd98f25705133","sha256:621d70d5ec276097e6f6ad71fc9267740846b0a5228f9ac185deea56a393aa75"],"state_sha256":"babab1c936b0b8da89d398afa5d1a0e09029db72344aef5df884ea7efd13bac0"}