{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ADIQ6AGJ36UK552FPS3Y2DD24Y","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":"c8748b78fd729b22cb6edec57b63d938e7a9d6a8c3b2088d704a7a85fc783a4f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-06T03:52:55Z","title_canon_sha256":"d51b8d20823f56d27cf1dc65af65289e1d87cb08e8b798830a368f2f9c57660a"},"schema_version":"1.0","source":{"id":"2508.04063","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.04063","created_at":"2026-07-05T11:49:20Z"},{"alias_kind":"arxiv_version","alias_value":"2508.04063v1","created_at":"2026-07-05T11:49:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04063","created_at":"2026-07-05T11:49:20Z"},{"alias_kind":"pith_short_12","alias_value":"ADIQ6AGJ36UK","created_at":"2026-07-05T11:49:20Z"},{"alias_kind":"pith_short_16","alias_value":"ADIQ6AGJ36UK552F","created_at":"2026-07-05T11:49:20Z"},{"alias_kind":"pith_short_8","alias_value":"ADIQ6AGJ","created_at":"2026-07-05T11:49:20Z"}],"graph_snapshots":[{"event_id":"sha256:31495adb9b694ed0291b0cbc57b97c3d019333e33f7897f9ce0f29f71ac758db","target":"graph","created_at":"2026-07-05T11:49:20Z","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/2508.04063/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Research to improve Automated Short Answer Grading has recently focused on Large Language Models (LLMs) with prompt engineering and no- or few-shot prompting to achieve best results. This is in contrast to the fine-tuning approach, which has historically required large-scale compute clusters inaccessible to most users. New closed-model approaches such as OpenAI's fine-tuning service promise results with as few as 100 examples, while methods using open weights such as quantized low-rank adaptive (QLORA) can be used to fine-tune models on consumer GPUs. We evaluate both of these fine-tuning meth","authors_text":"Benjamin Nye, Daniel Auerbach, Joel Walsh, Mark Core, Siddarth Mamidanna","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-06T03:52:55Z","title":"Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04063","kind":"arxiv","version":1},"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:fc4b4178597e376398bb4165f0f25caf7007c218490878518c2fa158e1a88a60","target":"record","created_at":"2026-07-05T11:49:20Z","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":"c8748b78fd729b22cb6edec57b63d938e7a9d6a8c3b2088d704a7a85fc783a4f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-06T03:52:55Z","title_canon_sha256":"d51b8d20823f56d27cf1dc65af65289e1d87cb08e8b798830a368f2f9c57660a"},"schema_version":"1.0","source":{"id":"2508.04063","kind":"arxiv","version":1}},"canonical_sha256":"00d10f00c9dfa8aef7457cb78d0c7ae61b0233d4948395a32c40c59f47d94502","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00d10f00c9dfa8aef7457cb78d0c7ae61b0233d4948395a32c40c59f47d94502","first_computed_at":"2026-07-05T11:49:20.044919Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:20.044919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZQhm66IAyMhcdC2bKEiXHrjqs1KLp+WbL0Km3xEaBH8g6HiEuRSAixGJk1ng9qGEC+GRzaR8ClMM/HEoMV67Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:20.045381Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.04063","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc4b4178597e376398bb4165f0f25caf7007c218490878518c2fa158e1a88a60","sha256:31495adb9b694ed0291b0cbc57b97c3d019333e33f7897f9ce0f29f71ac758db"],"state_sha256":"e48465010b41073fcbad07a0031f6ce67f695d8a4a466bbbf2db5a4846a54524"}