{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XCYTYQCC4FNA3X6FXE7ZX53NGE","short_pith_number":"pith:XCYTYQCC","schema_version":"1.0","canonical_sha256":"b8b13c4042e15a0ddfc5b93f9bf76d312345b9570c544c2e370b17b6854eb858","source":{"kind":"arxiv","id":"2501.05891","version":2},"attestation_state":"computed","paper":{"title":"Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bianca Raimondi, Maurizio Gabbrielli, Saverio Giallorenzo","submitted_at":"2025-01-10T11:44:35Z","abstract_excerpt":"In education, the capability of generating human-like text of Large Language Models (LLMs) inspired work on how they can increase the efficiency of learning and teaching. We study the affordability of these models for educators and students by investigating how LLMs answer multiple-choice questions (MCQs) with respect to hardware constraints and refinement techniques. We explore this space by using generic pre-trained LLMs (the 7B, 13B, and 70B variants of LLaMA-2) to answer 162 undergraduate-level MCQs from a course on Programming Languages (PL) -- the MCQ dataset is a contribution of this wo"},"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":"2501.05891","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-10T11:44:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"53078eb181cf97e91e166ca4b852d2902bd3d7061cacba4bb6f8a66d3372efce","abstract_canon_sha256":"065a58113522ad8c206b68a6e43d65cfd1d30371e18d747b527833420ad03d1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:29.833495Z","signature_b64":"rF0zie7gxjqIlHhpwH0iJgvG/wUViUN7Kt8aB5IIgT9HUEYja/yShOVugYdSVSFmrGbQywLlcOCFhX5Jzx0OBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8b13c4042e15a0ddfc5b93f9bf76d312345b9570c544c2e370b17b6854eb858","last_reissued_at":"2026-07-05T10:24:29.832999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:29.832999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bianca Raimondi, Maurizio Gabbrielli, Saverio Giallorenzo","submitted_at":"2025-01-10T11:44:35Z","abstract_excerpt":"In education, the capability of generating human-like text of Large Language Models (LLMs) inspired work on how they can increase the efficiency of learning and teaching. We study the affordability of these models for educators and students by investigating how LLMs answer multiple-choice questions (MCQs) with respect to hardware constraints and refinement techniques. We explore this space by using generic pre-trained LLMs (the 7B, 13B, and 70B variants of LLaMA-2) to answer 162 undergraduate-level MCQs from a course on Programming Languages (PL) -- the MCQ dataset is a contribution of this wo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.05891","kind":"arxiv","version":2},"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/2501.05891/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":"2501.05891","created_at":"2026-07-05T10:24:29.833060+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.05891v2","created_at":"2026-07-05T10:24:29.833060+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.05891","created_at":"2026-07-05T10:24:29.833060+00:00"},{"alias_kind":"pith_short_12","alias_value":"XCYTYQCC4FNA","created_at":"2026-07-05T10:24:29.833060+00:00"},{"alias_kind":"pith_short_16","alias_value":"XCYTYQCC4FNA3X6F","created_at":"2026-07-05T10:24:29.833060+00:00"},{"alias_kind":"pith_short_8","alias_value":"XCYTYQCC","created_at":"2026-07-05T10:24:29.833060+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE","json":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE.json","graph_json":"https://pith.science/api/pith-number/XCYTYQCC4FNA3X6FXE7ZX53NGE/graph.json","events_json":"https://pith.science/api/pith-number/XCYTYQCC4FNA3X6FXE7ZX53NGE/events.json","paper":"https://pith.science/paper/XCYTYQCC"},"agent_actions":{"view_html":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE","download_json":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE.json","view_paper":"https://pith.science/paper/XCYTYQCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.05891&json=true","fetch_graph":"https://pith.science/api/pith-number/XCYTYQCC4FNA3X6FXE7ZX53NGE/graph.json","fetch_events":"https://pith.science/api/pith-number/XCYTYQCC4FNA3X6FXE7ZX53NGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE/action/storage_attestation","attest_author":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE/action/author_attestation","sign_citation":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE/action/citation_signature","submit_replication":"https://pith.science/pith/XCYTYQCC4FNA3X6FXE7ZX53NGE/action/replication_record"}},"created_at":"2026-07-05T10:24:29.833060+00:00","updated_at":"2026-07-05T10:24:29.833060+00:00"}