{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YZ34LEWLO6CGW2PTE7TD7U64OV","short_pith_number":"pith:YZ34LEWL","schema_version":"1.0","canonical_sha256":"c677c592cb77846b69f327e63fd3dc757a455b8dcf2ee545305903927f495570","source":{"kind":"arxiv","id":"2409.02569","version":1},"attestation_state":"computed","paper":{"title":"More is More: Addition Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.HC"],"primary_cat":"cs.CL","authors_text":"Cristiano De Nobili, Luca Santagata","submitted_at":"2024-09-04T09:39:07Z","abstract_excerpt":"In this paper, we investigate the presence of additive bias in Large Language Models (LLMs), drawing a parallel to the cognitive bias observed in humans where individuals tend to favor additive over subtractive changes. Using a series of controlled experiments, we tested various LLMs, including GPT-3.5 Turbo, Claude 3.5 Sonnet, Mistral, Math$\\Sigma$tral, and Llama 3.1, on tasks designed to measure their propensity for additive versus subtractive modifications. Our findings demonstrate a significant preference for additive changes across all tested models. For example, in a palindrome creation "},"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":"2409.02569","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-04T09:39:07Z","cross_cats_sorted":["cs.AI","cs.CY","cs.HC"],"title_canon_sha256":"4c30dcaced82676c7da170a7ada33e068016445a9c87946201f796312c10a470","abstract_canon_sha256":"2aae24947e05b469adbe143e931b09b3f90f62c1138c1c1c18f17e61436a98dc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:07.027908Z","signature_b64":"jZmFbaZyk+ky2TpHtTJVoMSZY91nDK8dSrweCgHIS5OHIJ8MlDcDJD8Cd7E+P3tk7/DP6FT5mcubLs2zI1dsAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c677c592cb77846b69f327e63fd3dc757a455b8dcf2ee545305903927f495570","last_reissued_at":"2026-07-05T09:03:07.027359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:07.027359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"More is More: Addition Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.HC"],"primary_cat":"cs.CL","authors_text":"Cristiano De Nobili, Luca Santagata","submitted_at":"2024-09-04T09:39:07Z","abstract_excerpt":"In this paper, we investigate the presence of additive bias in Large Language Models (LLMs), drawing a parallel to the cognitive bias observed in humans where individuals tend to favor additive over subtractive changes. Using a series of controlled experiments, we tested various LLMs, including GPT-3.5 Turbo, Claude 3.5 Sonnet, Mistral, Math$\\Sigma$tral, and Llama 3.1, on tasks designed to measure their propensity for additive versus subtractive modifications. Our findings demonstrate a significant preference for additive changes across all tested models. For example, in a palindrome creation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02569","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/2409.02569/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":"2409.02569","created_at":"2026-07-05T09:03:07.027422+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.02569v1","created_at":"2026-07-05T09:03:07.027422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02569","created_at":"2026-07-05T09:03:07.027422+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZ34LEWLO6CG","created_at":"2026-07-05T09:03:07.027422+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZ34LEWLO6CGW2PT","created_at":"2026-07-05T09:03:07.027422+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZ34LEWL","created_at":"2026-07-05T09:03:07.027422+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10528","citing_title":"Collective Alignment in LLM Multi-Agent Systems: Disentangling Bias from Cooperation via Statistical Physics","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV","json":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV.json","graph_json":"https://pith.science/api/pith-number/YZ34LEWLO6CGW2PTE7TD7U64OV/graph.json","events_json":"https://pith.science/api/pith-number/YZ34LEWLO6CGW2PTE7TD7U64OV/events.json","paper":"https://pith.science/paper/YZ34LEWL"},"agent_actions":{"view_html":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV","download_json":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV.json","view_paper":"https://pith.science/paper/YZ34LEWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.02569&json=true","fetch_graph":"https://pith.science/api/pith-number/YZ34LEWLO6CGW2PTE7TD7U64OV/graph.json","fetch_events":"https://pith.science/api/pith-number/YZ34LEWLO6CGW2PTE7TD7U64OV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV/action/storage_attestation","attest_author":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV/action/author_attestation","sign_citation":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV/action/citation_signature","submit_replication":"https://pith.science/pith/YZ34LEWLO6CGW2PTE7TD7U64OV/action/replication_record"}},"created_at":"2026-07-05T09:03:07.027422+00:00","updated_at":"2026-07-05T09:03:07.027422+00:00"}