{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZM2XAQGD5QHY4OJ6LANSTZ3BNF","short_pith_number":"pith:ZM2XAQGD","canonical_record":{"source":{"id":"2404.00457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T19:43:45Z","cross_cats_sorted":[],"title_canon_sha256":"8185a9babda10b2e7df7e8be7313554add5086d8f94058fae806f14b6cbf4036","abstract_canon_sha256":"8b717025ca2770b652977a89cdd0a7b82ec872c22113797c1536e45801522490"},"schema_version":"1.0"},"canonical_sha256":"cb357040c3ec0f8e393e581b29e761696a082d595b0d155169af80124ecd1d4d","source":{"kind":"arxiv","id":"2404.00457","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.00457","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"arxiv_version","alias_value":"2404.00457v1","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00457","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_12","alias_value":"ZM2XAQGD5QHY","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZM2XAQGD5QHY4OJ6","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZM2XAQGD","created_at":"2026-07-05T08:02:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZM2XAQGD5QHY4OJ6LANSTZ3BNF","target":"record","payload":{"canonical_record":{"source":{"id":"2404.00457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T19:43:45Z","cross_cats_sorted":[],"title_canon_sha256":"8185a9babda10b2e7df7e8be7313554add5086d8f94058fae806f14b6cbf4036","abstract_canon_sha256":"8b717025ca2770b652977a89cdd0a7b82ec872c22113797c1536e45801522490"},"schema_version":"1.0"},"canonical_sha256":"cb357040c3ec0f8e393e581b29e761696a082d595b0d155169af80124ecd1d4d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:33.650750Z","signature_b64":"OJejWzlGssB4JIR7vLxAohZWNKt4f1R+A3V6lhDrO3o3GoQEu+1Ww96dGmsZnhVjKf6yi5KaGZSmFiMzjLjhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb357040c3ec0f8e393e581b29e761696a082d595b0d155169af80124ecd1d4d","last_reissued_at":"2026-07-05T08:02:33.650338Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:33.650338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.00457","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:02:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wn+ZlY9BzrJFl1ZRm0ay0QV2pRtV9ChCHZrCEYpvPrA3g85IBPYNfC0QXmPZEtHYCieokuMS4ZCxDxk/notuCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:33:44.794451Z"},"content_sha256":"8b5afa41feb340f7eff5b21a23e4997b8eafd41c78ffe5c901fc7af05ffa01eb","schema_version":"1.0","event_id":"sha256:8b5afa41feb340f7eff5b21a23e4997b8eafd41c78ffe5c901fc7af05ffa01eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZM2XAQGD5QHY4OJ6LANSTZ3BNF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Feng Yao, Jingbo Shang, Letian Peng, Zihan Wang, Zilong Wang","submitted_at":"2024-03-30T19:43:45Z","abstract_excerpt":"Information extraction (IE) is a fundamental area in natural language processing where prompting large language models (LLMs), even with in-context examples, cannot defeat small LMs tuned on very small IE datasets. We observe that IE tasks, such as named entity recognition and relation extraction, all focus on extracting important information, which can be formalized as a label-to-span matching. In this paper, we propose a novel framework MetaIE to build a small LM as meta-model by learning to extract \"important information\", i.e., the meta-understanding of IE, so that this meta-model can be a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00457","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/2404.00457/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":"2026-07-05T08:02:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WhmVi77cQQCRXQeg+u9+I76sClVLIaVf3QAkqXgxmVPCL0Lv4nxbjoYC4jbzsfnjgFG6ZbFsvz4455ylczrTBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:33:44.796860Z"},"content_sha256":"286989d7f13b63989759427e1f981ce2ce773bfd3119aedefe2f36415fe180f1","schema_version":"1.0","event_id":"sha256:286989d7f13b63989759427e1f981ce2ce773bfd3119aedefe2f36415fe180f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/bundle.json","state_url":"https://pith.science/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/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":"2026-08-08T23:33:44Z","links":{"resolver":"https://pith.science/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF","bundle":"https://pith.science/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/bundle.json","state":"https://pith.science/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZM2XAQGD5QHY4OJ6LANSTZ3BNF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZM2XAQGD5QHY4OJ6LANSTZ3BNF","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":"8b717025ca2770b652977a89cdd0a7b82ec872c22113797c1536e45801522490","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T19:43:45Z","title_canon_sha256":"8185a9babda10b2e7df7e8be7313554add5086d8f94058fae806f14b6cbf4036"},"schema_version":"1.0","source":{"id":"2404.00457","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.00457","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"arxiv_version","alias_value":"2404.00457v1","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00457","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_12","alias_value":"ZM2XAQGD5QHY","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZM2XAQGD5QHY4OJ6","created_at":"2026-07-05T08:02:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZM2XAQGD","created_at":"2026-07-05T08:02:33Z"}],"graph_snapshots":[{"event_id":"sha256:286989d7f13b63989759427e1f981ce2ce773bfd3119aedefe2f36415fe180f1","target":"graph","created_at":"2026-07-05T08:02:33Z","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/2404.00457/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Information extraction (IE) is a fundamental area in natural language processing where prompting large language models (LLMs), even with in-context examples, cannot defeat small LMs tuned on very small IE datasets. We observe that IE tasks, such as named entity recognition and relation extraction, all focus on extracting important information, which can be formalized as a label-to-span matching. In this paper, we propose a novel framework MetaIE to build a small LM as meta-model by learning to extract \"important information\", i.e., the meta-understanding of IE, so that this meta-model can be a","authors_text":"Feng Yao, Jingbo Shang, Letian Peng, Zihan Wang, Zilong Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T19:43:45Z","title":"MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00457","kind":"arxiv","version":1},"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":"sha256:8b5afa41feb340f7eff5b21a23e4997b8eafd41c78ffe5c901fc7af05ffa01eb","target":"record","created_at":"2026-07-05T08:02:33Z","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":"8b717025ca2770b652977a89cdd0a7b82ec872c22113797c1536e45801522490","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T19:43:45Z","title_canon_sha256":"8185a9babda10b2e7df7e8be7313554add5086d8f94058fae806f14b6cbf4036"},"schema_version":"1.0","source":{"id":"2404.00457","kind":"arxiv","version":1}},"canonical_sha256":"cb357040c3ec0f8e393e581b29e761696a082d595b0d155169af80124ecd1d4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cb357040c3ec0f8e393e581b29e761696a082d595b0d155169af80124ecd1d4d","first_computed_at":"2026-07-05T08:02:33.650338Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:33.650338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OJejWzlGssB4JIR7vLxAohZWNKt4f1R+A3V6lhDrO3o3GoQEu+1Ww96dGmsZnhVjKf6yi5KaGZSmFiMzjLjhBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:33.650750Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.00457","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b5afa41feb340f7eff5b21a23e4997b8eafd41c78ffe5c901fc7af05ffa01eb","sha256:286989d7f13b63989759427e1f981ce2ce773bfd3119aedefe2f36415fe180f1"],"state_sha256":"810a5e37fd4d8663f7ba3da03491b032b5c793f6733e569d7bfa06055fd14f94"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6efdyh6WnsA4hZGA5mWZAP7a6Prh8PI0F7DknSe252jirFNYwbU9/49OuQTbC/6JuPccxe5uokJQYxZpCihVDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:33:44.812513Z","bundle_sha256":"aa60144bbde2c2264b6629925debc819ae380a1d1db277bbcfbd5c30a42de357"}}