{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JEDYDDSZVVQJ3ZBINU2YBVC5QO","short_pith_number":"pith:JEDYDDSZ","schema_version":"1.0","canonical_sha256":"4907818e59ad609de4286d3580d45d839895333505230ed6cdc0e27d98dc0067","source":{"kind":"arxiv","id":"2504.14039","version":1},"attestation_state":"computed","paper":{"title":"MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jacob Haimes, Jaime Raldua Veuthey, Suhas Hariharan, Zainab Ali Majid","submitted_at":"2025-04-18T19:01:53Z","abstract_excerpt":"As Large Language Models (LLMs) advance, their potential for widespread societal impact grows simultaneously. Hence, rigorous LLM evaluations are both a technical necessity and social imperative. While numerous evaluation benchmarks have been developed, there remains a critical gap in meta-evaluation: effectively assessing benchmarks' quality. We propose MEQA, a framework for the meta-evaluation of question and answer (QA) benchmarks, to provide standardized assessments, quantifiable scores, and enable meaningful intra-benchmark comparisons. We demonstrate this approach on cybersecurity benchm"},"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":"2504.14039","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-18T19:01:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fe2a74a8c3ab0f1917225f3a1435f15206484becd0b17c5074d429d12d2f8f10","abstract_canon_sha256":"17d741feceba4dec2a2587580a08806beae852ee3049818edadedfb82dce32b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:09.971921Z","signature_b64":"kGphm2mv75x3Ian5zkE3xF4GqbhYYZ+q3gUzhBBp37vUPqkQocANvpe0JhOvD/lW4ej1KoPRo5v1i/UP0A6vCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4907818e59ad609de4286d3580d45d839895333505230ed6cdc0e27d98dc0067","last_reissued_at":"2026-07-05T10:51:09.971415Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:09.971415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jacob Haimes, Jaime Raldua Veuthey, Suhas Hariharan, Zainab Ali Majid","submitted_at":"2025-04-18T19:01:53Z","abstract_excerpt":"As Large Language Models (LLMs) advance, their potential for widespread societal impact grows simultaneously. Hence, rigorous LLM evaluations are both a technical necessity and social imperative. While numerous evaluation benchmarks have been developed, there remains a critical gap in meta-evaluation: effectively assessing benchmarks' quality. We propose MEQA, a framework for the meta-evaluation of question and answer (QA) benchmarks, to provide standardized assessments, quantifiable scores, and enable meaningful intra-benchmark comparisons. We demonstrate this approach on cybersecurity benchm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14039","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/2504.14039/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":"2504.14039","created_at":"2026-07-05T10:51:09.971485+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14039v1","created_at":"2026-07-05T10:51:09.971485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14039","created_at":"2026-07-05T10:51:09.971485+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEDYDDSZVVQJ","created_at":"2026-07-05T10:51:09.971485+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEDYDDSZVVQJ3ZBI","created_at":"2026-07-05T10:51:09.971485+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEDYDDSZ","created_at":"2026-07-05T10:51:09.971485+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.05424","citing_title":"Evaluating the Reliability of Multiple Large Language Models in Risk Assessment: A CIS Controls Based Approach","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO","json":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO.json","graph_json":"https://pith.science/api/pith-number/JEDYDDSZVVQJ3ZBINU2YBVC5QO/graph.json","events_json":"https://pith.science/api/pith-number/JEDYDDSZVVQJ3ZBINU2YBVC5QO/events.json","paper":"https://pith.science/paper/JEDYDDSZ"},"agent_actions":{"view_html":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO","download_json":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO.json","view_paper":"https://pith.science/paper/JEDYDDSZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14039&json=true","fetch_graph":"https://pith.science/api/pith-number/JEDYDDSZVVQJ3ZBINU2YBVC5QO/graph.json","fetch_events":"https://pith.science/api/pith-number/JEDYDDSZVVQJ3ZBINU2YBVC5QO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO/action/storage_attestation","attest_author":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO/action/author_attestation","sign_citation":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO/action/citation_signature","submit_replication":"https://pith.science/pith/JEDYDDSZVVQJ3ZBINU2YBVC5QO/action/replication_record"}},"created_at":"2026-07-05T10:51:09.971485+00:00","updated_at":"2026-07-05T10:51:09.971485+00:00"}