{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5Z7KGHKFFPLY6P5YUREKWTSZHB","short_pith_number":"pith:5Z7KGHKF","canonical_record":{"source":{"id":"2310.02842","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-04T14:11:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c52e25fec02785f0b2e966fdfe6d0981e0c629eddd46f5fdfbe1fc761300efff","abstract_canon_sha256":"e8c011a1f80a8627081022dee786950a1f5660eb60ddf0e6624f63ad2b43b0b4"},"schema_version":"1.0"},"canonical_sha256":"ee7ea31d452bd78f3fb8a448ab4e59387ba5283f48d7667bb67c1d09cf057cb8","source":{"kind":"arxiv","id":"2310.02842","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02842","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02842v3","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02842","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_12","alias_value":"5Z7KGHKFFPLY","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_16","alias_value":"5Z7KGHKFFPLY6P5Y","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_8","alias_value":"5Z7KGHKF","created_at":"2026-07-05T10:02:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5Z7KGHKFFPLY6P5YUREKWTSZHB","target":"record","payload":{"canonical_record":{"source":{"id":"2310.02842","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-04T14:11:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c52e25fec02785f0b2e966fdfe6d0981e0c629eddd46f5fdfbe1fc761300efff","abstract_canon_sha256":"e8c011a1f80a8627081022dee786950a1f5660eb60ddf0e6624f63ad2b43b0b4"},"schema_version":"1.0"},"canonical_sha256":"ee7ea31d452bd78f3fb8a448ab4e59387ba5283f48d7667bb67c1d09cf057cb8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:21.631070Z","signature_b64":"PwTior6+zy7mZdfRfx5ae7qYLT2zarljnnWobBRd3YzlqLOtt4CQ721YtT+KlRlN4Hf5NSFHp9fhY9KXeg+BCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee7ea31d452bd78f3fb8a448ab4e59387ba5283f48d7667bb67c1d09cf057cb8","last_reissued_at":"2026-07-05T10:02:21.630415Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:21.630415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.02842","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:02:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3M1CmuWQOos/X80jDUCcYm5S+btnEtBJkyWY7KpZvFDBGT55awbBJpF76Xk+2sHieBMGiA6csrTfBK3ZC3sPAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T03:57:53.377104Z"},"content_sha256":"6068e87f39e9c19ccc42d18bd6a233436cea7ab14845b734801398eff03f69a2","schema_version":"1.0","event_id":"sha256:6068e87f39e9c19ccc42d18bd6a233436cea7ab14845b734801398eff03f69a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5Z7KGHKFFPLY6P5YUREKWTSZHB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ahmed Hassan Awadallah, Anastasios Kyrillidis, Chen Dun, Guoqing Zheng, Mirian Hipolito Garcia, Robert Sim","submitted_at":"2023-10-04T14:11:12Z","abstract_excerpt":"Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current trend is to use prompt instruction tuning to better adjust monolithic, pretrained LLMs for new -- but often individual -- downstream tasks. Thus, how one would expand prompt tuning to handle -- concomitantly -- heterogeneous tasks and data distributions is a widely open question. To address this gap, we suggest the use of \\emph{Mixture of Prompts}"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02842","kind":"arxiv","version":3},"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/2310.02842/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":"2026-07-05T10:02:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Iz/Xsdfl+HtcySP/aCtEQH01qfg6lST8jhOkz6lJ9TribTYE0Y6XKpups0e50KekmV0hTOtaeseA8z4iilM4AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T03:57:53.377487Z"},"content_sha256":"1df765ec9293f2dfdd70ae2847b7d2db9c12ae68fed0c340772ab5e42a9ee25c","schema_version":"1.0","event_id":"sha256:1df765ec9293f2dfdd70ae2847b7d2db9c12ae68fed0c340772ab5e42a9ee25c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/bundle.json","state_url":"https://pith.science/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/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":"2026-07-24T03:57:53Z","links":{"resolver":"https://pith.science/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB","bundle":"https://pith.science/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/bundle.json","state":"https://pith.science/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5Z7KGHKFFPLY6P5YUREKWTSZHB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5Z7KGHKFFPLY6P5YUREKWTSZHB","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":"e8c011a1f80a8627081022dee786950a1f5660eb60ddf0e6624f63ad2b43b0b4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-04T14:11:12Z","title_canon_sha256":"c52e25fec02785f0b2e966fdfe6d0981e0c629eddd46f5fdfbe1fc761300efff"},"schema_version":"1.0","source":{"id":"2310.02842","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02842","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02842v3","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02842","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_12","alias_value":"5Z7KGHKFFPLY","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_16","alias_value":"5Z7KGHKFFPLY6P5Y","created_at":"2026-07-05T10:02:21Z"},{"alias_kind":"pith_short_8","alias_value":"5Z7KGHKF","created_at":"2026-07-05T10:02:21Z"}],"graph_snapshots":[{"event_id":"sha256:1df765ec9293f2dfdd70ae2847b7d2db9c12ae68fed0c340772ab5e42a9ee25c","target":"graph","created_at":"2026-07-05T10:02:21Z","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/2310.02842/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current trend is to use prompt instruction tuning to better adjust monolithic, pretrained LLMs for new -- but often individual -- downstream tasks. Thus, how one would expand prompt tuning to handle -- concomitantly -- heterogeneous tasks and data distributions is a widely open question. To address this gap, we suggest the use of \\emph{Mixture of Prompts}","authors_text":"Ahmed Hassan Awadallah, Anastasios Kyrillidis, Chen Dun, Guoqing Zheng, Mirian Hipolito Garcia, Robert Sim","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-04T14:11:12Z","title":"Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02842","kind":"arxiv","version":3},"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":"sha256:6068e87f39e9c19ccc42d18bd6a233436cea7ab14845b734801398eff03f69a2","target":"record","created_at":"2026-07-05T10:02:21Z","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":"e8c011a1f80a8627081022dee786950a1f5660eb60ddf0e6624f63ad2b43b0b4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-04T14:11:12Z","title_canon_sha256":"c52e25fec02785f0b2e966fdfe6d0981e0c629eddd46f5fdfbe1fc761300efff"},"schema_version":"1.0","source":{"id":"2310.02842","kind":"arxiv","version":3}},"canonical_sha256":"ee7ea31d452bd78f3fb8a448ab4e59387ba5283f48d7667bb67c1d09cf057cb8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee7ea31d452bd78f3fb8a448ab4e59387ba5283f48d7667bb67c1d09cf057cb8","first_computed_at":"2026-07-05T10:02:21.630415Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:21.630415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PwTior6+zy7mZdfRfx5ae7qYLT2zarljnnWobBRd3YzlqLOtt4CQ721YtT+KlRlN4Hf5NSFHp9fhY9KXeg+BCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:21.631070Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.02842","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6068e87f39e9c19ccc42d18bd6a233436cea7ab14845b734801398eff03f69a2","sha256:1df765ec9293f2dfdd70ae2847b7d2db9c12ae68fed0c340772ab5e42a9ee25c"],"state_sha256":"a7856cfcf1cc238f6ef77df9237d4bcfff3aee54cd6b4a4f904c2f83c48f7119"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yx6vjWEZcGy/CESCB15FksmC1E6TbC4AleJ/DiqLHZt18FBQmBTkIDUomzzXr8L6rM+dZW48cHgaUoT2X3xhAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T03:57:53.379947Z","bundle_sha256":"1dafcd144b3d3af629d898c1b5a45f9ed649be94f839199820287637fbe304f8"}}