{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RHB2BRKINY64SIMBEJJ6LLMUOK","short_pith_number":"pith:RHB2BRKI","schema_version":"1.0","canonical_sha256":"89c3a0c5486e3dc921812253e5ad9472896c009b19b61d2c19ea342f4b0e96a0","source":{"kind":"arxiv","id":"2311.09755","version":2},"attestation_state":"computed","paper":{"title":"On the Impact of Calibration Data in Post-training Quantization and Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Miles Williams, Nikolaos Aletras","submitted_at":"2023-11-16T10:30:00Z","abstract_excerpt":"Quantization and pruning form the foundation of compression for neural networks, enabling efficient inference for large language models (LLMs). Recently, various quantization and pruning techniques have demonstrated remarkable performance in a post-training setting. They rely upon calibration data, a small set of unlabeled examples that are used to generate layer activations. However, no prior work has systematically investigated how the calibration data impacts the effectiveness of model compression methods. In this paper, we present the first extensive empirical study on the effect of calibr"},"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":"2311.09755","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-16T10:30:00Z","cross_cats_sorted":[],"title_canon_sha256":"a281e63ed5f531c2e9b3767e40a044a8baba386b23cce5d2949322579b10bb51","abstract_canon_sha256":"88e69767c160996f08ed316215b1a567df1914c7c7480867678bad8eedfe593c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:57.689740Z","signature_b64":"F2XgDRtwQiNQlk0bmj6/WLu7uehkevxN2xEMb1+y1qiXr7YnsNJy1/XDRjir5U02FgWFKL1DGvOGeukx5M4hCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89c3a0c5486e3dc921812253e5ad9472896c009b19b61d2c19ea342f4b0e96a0","last_reissued_at":"2026-07-05T09:30:57.689163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:57.689163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Impact of Calibration Data in Post-training Quantization and Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Miles Williams, Nikolaos Aletras","submitted_at":"2023-11-16T10:30:00Z","abstract_excerpt":"Quantization and pruning form the foundation of compression for neural networks, enabling efficient inference for large language models (LLMs). Recently, various quantization and pruning techniques have demonstrated remarkable performance in a post-training setting. They rely upon calibration data, a small set of unlabeled examples that are used to generate layer activations. However, no prior work has systematically investigated how the calibration data impacts the effectiveness of model compression methods. In this paper, we present the first extensive empirical study on the effect of calibr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09755","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/2311.09755/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":"2311.09755","created_at":"2026-07-05T09:30:57.689230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.09755v2","created_at":"2026-07-05T09:30:57.689230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09755","created_at":"2026-07-05T09:30:57.689230+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHB2BRKINY64","created_at":"2026-07-05T09:30:57.689230+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHB2BRKINY64SIMB","created_at":"2026-07-05T09:30:57.689230+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHB2BRKI","created_at":"2026-07-05T09:30:57.689230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08565","citing_title":"EinSort: Sorting is All We Need for Tensorizing LLM","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24008","citing_title":"Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK","json":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK.json","graph_json":"https://pith.science/api/pith-number/RHB2BRKINY64SIMBEJJ6LLMUOK/graph.json","events_json":"https://pith.science/api/pith-number/RHB2BRKINY64SIMBEJJ6LLMUOK/events.json","paper":"https://pith.science/paper/RHB2BRKI"},"agent_actions":{"view_html":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK","download_json":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK.json","view_paper":"https://pith.science/paper/RHB2BRKI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.09755&json=true","fetch_graph":"https://pith.science/api/pith-number/RHB2BRKINY64SIMBEJJ6LLMUOK/graph.json","fetch_events":"https://pith.science/api/pith-number/RHB2BRKINY64SIMBEJJ6LLMUOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK/action/storage_attestation","attest_author":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK/action/author_attestation","sign_citation":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK/action/citation_signature","submit_replication":"https://pith.science/pith/RHB2BRKINY64SIMBEJJ6LLMUOK/action/replication_record"}},"created_at":"2026-07-05T09:30:57.689230+00:00","updated_at":"2026-07-05T09:30:57.689230+00:00"}