{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:E4FUYHT4DFWU7N3HAOGRFYJVB6","short_pith_number":"pith:E4FUYHT4","canonical_record":{"source":{"id":"2310.14607","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T06:31:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e4142affb22f0fb665c9f2de74a985c0db74ae427f5abcb2987174a486e83c18","abstract_canon_sha256":"c1600e2360f7e57ca9ac7564b08eec139a394657fd97260c43f27438d36100b5"},"schema_version":"1.0"},"canonical_sha256":"270b4c1e7c196d4fb767038d12e1350f87c0fee2f1dbe52dbd234f21561a95f4","source":{"kind":"arxiv","id":"2310.14607","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14607","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14607v2","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14607","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"E4FUYHT4DFWU","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_16","alias_value":"E4FUYHT4DFWU7N3H","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_8","alias_value":"E4FUYHT4","created_at":"2026-07-05T08:03:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:E4FUYHT4DFWU7N3HAOGRFYJVB6","target":"record","payload":{"canonical_record":{"source":{"id":"2310.14607","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T06:31:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e4142affb22f0fb665c9f2de74a985c0db74ae427f5abcb2987174a486e83c18","abstract_canon_sha256":"c1600e2360f7e57ca9ac7564b08eec139a394657fd97260c43f27438d36100b5"},"schema_version":"1.0"},"canonical_sha256":"270b4c1e7c196d4fb767038d12e1350f87c0fee2f1dbe52dbd234f21561a95f4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:45.004898Z","signature_b64":"s60ecfelSj94XYQaV5TrHs9cJ2NIR1ODKq9fWt2pTxFOswueQwxoEEJVmZHtauZeb9vjeDK/ahW+BdyuQoM1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"270b4c1e7c196d4fb767038d12e1350f87c0fee2f1dbe52dbd234f21561a95f4","last_reissued_at":"2026-07-05T08:03:45.004417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:45.004417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.14607","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p35o7Ydd9H2oUZ4hzZEoz03PFgntxmfXdgA4qGTKnDYArYoSMQlcmoNAKO4ojFLTiMau7ilyTYRPCsKD+6d4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:00:50.367847Z"},"content_sha256":"0ab46b4a986f76512b4ebdfafec9bcaa4ad3bfa30ad6ab9ea2c5a7391b0dbf0c","schema_version":"1.0","event_id":"sha256:0ab46b4a986f76512b4ebdfafec9bcaa4ad3bfa30ad6ab9ea2c5a7391b0dbf0c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:E4FUYHT4DFWU7N3HAOGRFYJVB6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Himabindu Lakkaraju, Jiaqi Ma, Srishti Gautam, Yanchen Liu","submitted_at":"2023-10-23T06:31:28Z","abstract_excerpt":"Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is important to explore the following questions: what sources of information do LLMs draw upon when making classifications for tabular tasks; whether and to what extent are LLM classifications for tabular data influenced by social biases and stereot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14607","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/2310.14607/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CFpye6/urY1b0GwmHpKQly/lsaW96688rO29OwD1QXIbwdlf5rCsK0+VdQCZDROxpUyean9R83HiTUG/sxD2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:00:50.368839Z"},"content_sha256":"dfd0887debc84196239cdee9297fa4013440bb0ca1a15f800872b39dae83c984","schema_version":"1.0","event_id":"sha256:dfd0887debc84196239cdee9297fa4013440bb0ca1a15f800872b39dae83c984"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/bundle.json","state_url":"https://pith.science/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T14:00:50Z","links":{"resolver":"https://pith.science/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6","bundle":"https://pith.science/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/bundle.json","state":"https://pith.science/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E4FUYHT4DFWU7N3HAOGRFYJVB6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:E4FUYHT4DFWU7N3HAOGRFYJVB6","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":"c1600e2360f7e57ca9ac7564b08eec139a394657fd97260c43f27438d36100b5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T06:31:28Z","title_canon_sha256":"e4142affb22f0fb665c9f2de74a985c0db74ae427f5abcb2987174a486e83c18"},"schema_version":"1.0","source":{"id":"2310.14607","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14607","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14607v2","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14607","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"E4FUYHT4DFWU","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_16","alias_value":"E4FUYHT4DFWU7N3H","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_8","alias_value":"E4FUYHT4","created_at":"2026-07-05T08:03:45Z"}],"graph_snapshots":[{"event_id":"sha256:dfd0887debc84196239cdee9297fa4013440bb0ca1a15f800872b39dae83c984","target":"graph","created_at":"2026-07-05T08:03:45Z","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/2310.14607/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is important to explore the following questions: what sources of information do LLMs draw upon when making classifications for tabular tasks; whether and to what extent are LLM classifications for tabular data influenced by social biases and stereot","authors_text":"Himabindu Lakkaraju, Jiaqi Ma, Srishti Gautam, Yanchen Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T06:31:28Z","title":"Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14607","kind":"arxiv","version":2},"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":"sha256:0ab46b4a986f76512b4ebdfafec9bcaa4ad3bfa30ad6ab9ea2c5a7391b0dbf0c","target":"record","created_at":"2026-07-05T08:03:45Z","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":"c1600e2360f7e57ca9ac7564b08eec139a394657fd97260c43f27438d36100b5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T06:31:28Z","title_canon_sha256":"e4142affb22f0fb665c9f2de74a985c0db74ae427f5abcb2987174a486e83c18"},"schema_version":"1.0","source":{"id":"2310.14607","kind":"arxiv","version":2}},"canonical_sha256":"270b4c1e7c196d4fb767038d12e1350f87c0fee2f1dbe52dbd234f21561a95f4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"270b4c1e7c196d4fb767038d12e1350f87c0fee2f1dbe52dbd234f21561a95f4","first_computed_at":"2026-07-05T08:03:45.004417Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:45.004417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s60ecfelSj94XYQaV5TrHs9cJ2NIR1ODKq9fWt2pTxFOswueQwxoEEJVmZHtauZeb9vjeDK/ahW+BdyuQoM1Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:45.004898Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.14607","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ab46b4a986f76512b4ebdfafec9bcaa4ad3bfa30ad6ab9ea2c5a7391b0dbf0c","sha256:dfd0887debc84196239cdee9297fa4013440bb0ca1a15f800872b39dae83c984"],"state_sha256":"f20f5e4d77727418226e32e1b14596fa7041bce76fd423e60ad79b444be0545c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ty5hyEXlON4qGJryct7D3fFbtk/vXeu+UWg8PSo6gWUEzAqR/htk7Q3/YjwR5epaxH9fzZ2+KFIVRg+MclX6Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T14:00:50.378500Z","bundle_sha256":"8027b496a5754b13394e3eb4bc0e8e5fd14ddb4f58db120cb10be3421a1641cc"}}