{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HIFH4KG35XT7BW5U3G5WAFMM2F","short_pith_number":"pith:HIFH4KG3","canonical_record":{"source":{"id":"2205.11961","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-24T10:48:33Z","cross_cats_sorted":[],"title_canon_sha256":"602b6d23113723aa41ed7c8aa4ba6e982047958ced75c312f5a9cc17ef4c7a8c","abstract_canon_sha256":"53c16da4b9551ccf0a21935302f3755cf71306cdfb08f355f68cb3b13a64acff"},"schema_version":"1.0"},"canonical_sha256":"3a0a7e28dbede7f0dbb4d9bb60158cd1466db58f817b08b2385208ebc72eba4a","source":{"kind":"arxiv","id":"2205.11961","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11961","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11961v2","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11961","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"HIFH4KG35XT7","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"HIFH4KG35XT7BW5U","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"HIFH4KG3","created_at":"2026-07-05T05:21:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HIFH4KG35XT7BW5U3G5WAFMM2F","target":"record","payload":{"canonical_record":{"source":{"id":"2205.11961","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-24T10:48:33Z","cross_cats_sorted":[],"title_canon_sha256":"602b6d23113723aa41ed7c8aa4ba6e982047958ced75c312f5a9cc17ef4c7a8c","abstract_canon_sha256":"53c16da4b9551ccf0a21935302f3755cf71306cdfb08f355f68cb3b13a64acff"},"schema_version":"1.0"},"canonical_sha256":"3a0a7e28dbede7f0dbb4d9bb60158cd1466db58f817b08b2385208ebc72eba4a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:25.673794Z","signature_b64":"JhrtbVfInRGewAAc9SoKJH7D9ck5J5f+R+mHMbyQfFSGGJ0MqZQijoNi+7KSHBPKDINGoBveH7OV1JTsNMaWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a0a7e28dbede7f0dbb4d9bb60158cd1466db58f817b08b2385208ebc72eba4a","last_reissued_at":"2026-07-05T05:21:25.673292Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:25.673292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.11961","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":"2026-07-05T05:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P4E+GrVmUSpMYPnLRHh8EkdIm74X2LVcK3di+R17PTaYJw7nHWX0t9xxCV5h5gtMJdiIkimzES5ij846ORPqCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T14:21:57.239375Z"},"content_sha256":"086b7e0623501f1aca75b758e3fed9ad3f87aff35b901798bef5f3ff17bf22bb","schema_version":"1.0","event_id":"sha256:086b7e0623501f1aca75b758e3fed9ad3f87aff35b901798bef5f3ff17bf22bb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HIFH4KG35XT7BW5U3G5WAFMM2F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Akari Asai, Hannaneh Hajishirzi, Matthew E. Peters, Mohammadreza Salehi","submitted_at":"2022-05-24T10:48:33Z","abstract_excerpt":"This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11961","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/2205.11961/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":"2026-07-05T05:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTfLXJYaQtrPsxkHsWocNas2gfp2JYYvrLDZHGXRF55c9mItGqhHXmj9Jz9v8h6E2YPzPyMEvPRuBm7+YXp1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T14:21:57.240231Z"},"content_sha256":"b592bfa869d41363f00b4e4eba7136bf0112ff6a59d2261242be5969ae0f38f2","schema_version":"1.0","event_id":"sha256:b592bfa869d41363f00b4e4eba7136bf0112ff6a59d2261242be5969ae0f38f2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/bundle.json","state_url":"https://pith.science/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/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":"2026-08-11T14:21:57Z","links":{"resolver":"https://pith.science/pith/HIFH4KG35XT7BW5U3G5WAFMM2F","bundle":"https://pith.science/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/bundle.json","state":"https://pith.science/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HIFH4KG35XT7BW5U3G5WAFMM2F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HIFH4KG35XT7BW5U3G5WAFMM2F","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":"53c16da4b9551ccf0a21935302f3755cf71306cdfb08f355f68cb3b13a64acff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-24T10:48:33Z","title_canon_sha256":"602b6d23113723aa41ed7c8aa4ba6e982047958ced75c312f5a9cc17ef4c7a8c"},"schema_version":"1.0","source":{"id":"2205.11961","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11961","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11961v2","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11961","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"HIFH4KG35XT7","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"HIFH4KG35XT7BW5U","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"HIFH4KG3","created_at":"2026-07-05T05:21:25Z"}],"graph_snapshots":[{"event_id":"sha256:b592bfa869d41363f00b4e4eba7136bf0112ff6a59d2261242be5969ae0f38f2","target":"graph","created_at":"2026-07-05T05:21:25Z","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/2205.11961/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and","authors_text":"Akari Asai, Hannaneh Hajishirzi, Matthew E. Peters, Mohammadreza Salehi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-24T10:48:33Z","title":"ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11961","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":"sha256:086b7e0623501f1aca75b758e3fed9ad3f87aff35b901798bef5f3ff17bf22bb","target":"record","created_at":"2026-07-05T05:21:25Z","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":"53c16da4b9551ccf0a21935302f3755cf71306cdfb08f355f68cb3b13a64acff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-24T10:48:33Z","title_canon_sha256":"602b6d23113723aa41ed7c8aa4ba6e982047958ced75c312f5a9cc17ef4c7a8c"},"schema_version":"1.0","source":{"id":"2205.11961","kind":"arxiv","version":2}},"canonical_sha256":"3a0a7e28dbede7f0dbb4d9bb60158cd1466db58f817b08b2385208ebc72eba4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a0a7e28dbede7f0dbb4d9bb60158cd1466db58f817b08b2385208ebc72eba4a","first_computed_at":"2026-07-05T05:21:25.673292Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:21:25.673292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JhrtbVfInRGewAAc9SoKJH7D9ck5J5f+R+mHMbyQfFSGGJ0MqZQijoNi+7KSHBPKDINGoBveH7OV1JTsNMaWBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:21:25.673794Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.11961","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:086b7e0623501f1aca75b758e3fed9ad3f87aff35b901798bef5f3ff17bf22bb","sha256:b592bfa869d41363f00b4e4eba7136bf0112ff6a59d2261242be5969ae0f38f2"],"state_sha256":"c61adca9a6031c4cd71b16321d965f07ec8bc96af1af60e0d54ce30e868d48d9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3FUE3UrokZSzEs2lDH0GU3QKJW+vnfxP9RJ+PdbHQ1p8HJwQviFRP7zBKHwR56AuYWVBpCB4mm86RRSUVzWLDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T14:21:57.248291Z","bundle_sha256":"05f28657bcfe547d123da5cd4388c10b4d0bcd1ac05945c67ce983fcf6bcf6e6"}}