{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KLRODRIMTZXWTBWZZOIX66ABFX","short_pith_number":"pith:KLRODRIM","canonical_record":{"source":{"id":"2408.02103","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-04T18:08:15Z","cross_cats_sorted":[],"title_canon_sha256":"276ecae455338a7c25d7a8d3d827dad57cf5f51da286a6593faab2cd3b77ff1e","abstract_canon_sha256":"b65f433927f1371b4ab7cbfc42b13913cca0d76e2ff958d83d3d1669912b42a3"},"schema_version":"1.0"},"canonical_sha256":"52e2e1c50c9e6f6986d9cb917f78012de904f04209d04de02026e5a780cf0f17","source":{"kind":"arxiv","id":"2408.02103","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.02103","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"arxiv_version","alias_value":"2408.02103v1","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02103","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_12","alias_value":"KLRODRIMTZXW","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_16","alias_value":"KLRODRIMTZXWTBWZ","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_8","alias_value":"KLRODRIM","created_at":"2026-07-05T08:52:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KLRODRIMTZXWTBWZZOIX66ABFX","target":"record","payload":{"canonical_record":{"source":{"id":"2408.02103","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-04T18:08:15Z","cross_cats_sorted":[],"title_canon_sha256":"276ecae455338a7c25d7a8d3d827dad57cf5f51da286a6593faab2cd3b77ff1e","abstract_canon_sha256":"b65f433927f1371b4ab7cbfc42b13913cca0d76e2ff958d83d3d1669912b42a3"},"schema_version":"1.0"},"canonical_sha256":"52e2e1c50c9e6f6986d9cb917f78012de904f04209d04de02026e5a780cf0f17","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:07.515678Z","signature_b64":"Bbzah5UyheP2YWvL91eCKFvZ6plIfKmBpp1QOk/48KGcNrYXwBGGgh0qlHIMzxFb3QuP3Ib94rbXWZksNWwtBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52e2e1c50c9e6f6986d9cb917f78012de904f04209d04de02026e5a780cf0f17","last_reissued_at":"2026-07-05T08:52:07.515287Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:07.515287Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.02103","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":"2026-07-05T08:52:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t1YD4pRtt+IulBsinbf6gHXg2dkGqbnRrlAtc6Ku+dICIzCgw2KI9P97WvB1+ppxMQsxyj1MxVsiq6aP6cqyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:04:49.663384Z"},"content_sha256":"a3e9a6eb70556207aacc0aeda484adc67d15ec9358696fa220401b47aa1fba20","schema_version":"1.0","event_id":"sha256:a3e9a6eb70556207aacc0aeda484adc67d15ec9358696fa220401b47aa1fba20"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KLRODRIMTZXWTBWZZOIX66ABFX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chao Lou, Pengjun Xie, Peng Wang, Shengyu Mao, Xiaobin Wang, Yong Jiang","submitted_at":"2024-08-04T18:08:15Z","abstract_excerpt":"In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the remarkable ICL capabilities demonstrated by Large Language Models (LLMs), existing works are highly dependent on large-scale labeled support sets, not always feasible in practical scenarios. To refine this approach, we focus primarily on an innovative selective annotation mechanism, which precedes the standard demonstration retrieval. We introduce the Language Model-based Determinant Point Process (LM-DPP) that simult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02103","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/2408.02103/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":"2026-07-05T08:52:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gYYZbo7SB1beQxSCbKDAebuPQ4kwRsWJvQMroZW04CBs61Mtp4iYh4vmqXKRtL+rOys+BqZKAA1/IpZD7oNzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:04:49.663720Z"},"content_sha256":"714cdb43f7388144283e6d91bd2d6b74951e542af23dd261ec325ada72c9c183","schema_version":"1.0","event_id":"sha256:714cdb43f7388144283e6d91bd2d6b74951e542af23dd261ec325ada72c9c183"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KLRODRIMTZXWTBWZZOIX66ABFX/bundle.json","state_url":"https://pith.science/pith/KLRODRIMTZXWTBWZZOIX66ABFX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KLRODRIMTZXWTBWZZOIX66ABFX/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":"2026-08-08T01:04:49Z","links":{"resolver":"https://pith.science/pith/KLRODRIMTZXWTBWZZOIX66ABFX","bundle":"https://pith.science/pith/KLRODRIMTZXWTBWZZOIX66ABFX/bundle.json","state":"https://pith.science/pith/KLRODRIMTZXWTBWZZOIX66ABFX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KLRODRIMTZXWTBWZZOIX66ABFX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KLRODRIMTZXWTBWZZOIX66ABFX","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":"b65f433927f1371b4ab7cbfc42b13913cca0d76e2ff958d83d3d1669912b42a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-04T18:08:15Z","title_canon_sha256":"276ecae455338a7c25d7a8d3d827dad57cf5f51da286a6593faab2cd3b77ff1e"},"schema_version":"1.0","source":{"id":"2408.02103","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.02103","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"arxiv_version","alias_value":"2408.02103v1","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02103","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_12","alias_value":"KLRODRIMTZXW","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_16","alias_value":"KLRODRIMTZXWTBWZ","created_at":"2026-07-05T08:52:07Z"},{"alias_kind":"pith_short_8","alias_value":"KLRODRIM","created_at":"2026-07-05T08:52:07Z"}],"graph_snapshots":[{"event_id":"sha256:714cdb43f7388144283e6d91bd2d6b74951e542af23dd261ec325ada72c9c183","target":"graph","created_at":"2026-07-05T08:52:07Z","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/2408.02103/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the remarkable ICL capabilities demonstrated by Large Language Models (LLMs), existing works are highly dependent on large-scale labeled support sets, not always feasible in practical scenarios. To refine this approach, we focus primarily on an innovative selective annotation mechanism, which precedes the standard demonstration retrieval. We introduce the Language Model-based Determinant Point Process (LM-DPP) that simult","authors_text":"Chao Lou, Pengjun Xie, Peng Wang, Shengyu Mao, Xiaobin Wang, Yong Jiang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-04T18:08:15Z","title":"Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02103","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:a3e9a6eb70556207aacc0aeda484adc67d15ec9358696fa220401b47aa1fba20","target":"record","created_at":"2026-07-05T08:52:07Z","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":"b65f433927f1371b4ab7cbfc42b13913cca0d76e2ff958d83d3d1669912b42a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-04T18:08:15Z","title_canon_sha256":"276ecae455338a7c25d7a8d3d827dad57cf5f51da286a6593faab2cd3b77ff1e"},"schema_version":"1.0","source":{"id":"2408.02103","kind":"arxiv","version":1}},"canonical_sha256":"52e2e1c50c9e6f6986d9cb917f78012de904f04209d04de02026e5a780cf0f17","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"52e2e1c50c9e6f6986d9cb917f78012de904f04209d04de02026e5a780cf0f17","first_computed_at":"2026-07-05T08:52:07.515287Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:07.515287Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Bbzah5UyheP2YWvL91eCKFvZ6plIfKmBpp1QOk/48KGcNrYXwBGGgh0qlHIMzxFb3QuP3Ib94rbXWZksNWwtBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:07.515678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.02103","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3e9a6eb70556207aacc0aeda484adc67d15ec9358696fa220401b47aa1fba20","sha256:714cdb43f7388144283e6d91bd2d6b74951e542af23dd261ec325ada72c9c183"],"state_sha256":"c4470c72067ae29ea27b8769c1f81fb6c649c98cebfba3bf2c7b558501cd5a2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rd6YQeLa7kLQzrlAyj1+p7TWwVGPBFZYLFzzKP7Ie/1d68zHEzFVRqSlccS4llDG5cPjFZTtZE5zVOp3NJMEAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T01:04:49.668707Z","bundle_sha256":"9a52a22596ce964b7089fd247e5d90c4859614f12380c14fcba36cb984d767fa"}}