{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2NJOPMRQVUNHAHFHTW5NC7PCRO","short_pith_number":"pith:2NJOPMRQ","canonical_record":{"source":{"id":"2404.17832","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T08:53:58Z","cross_cats_sorted":[],"title_canon_sha256":"c144ebe2f27dab2043e573488322e744d0f8aed0446b9334a7f03538c98f3a4b","abstract_canon_sha256":"c305d9252239407b85eeda854d3b3cf7c38bb239f38d3014ffe0e1a5724620ce"},"schema_version":"1.0"},"canonical_sha256":"d352e7b230ad1a701ca79dbad17de28bb759b6732f26e75acd021402ea00b067","source":{"kind":"arxiv","id":"2404.17832","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.17832","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"arxiv_version","alias_value":"2404.17832v1","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.17832","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_12","alias_value":"2NJOPMRQVUNH","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_16","alias_value":"2NJOPMRQVUNHAHFH","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_8","alias_value":"2NJOPMRQ","created_at":"2026-07-05T08:12:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2NJOPMRQVUNHAHFHTW5NC7PCRO","target":"record","payload":{"canonical_record":{"source":{"id":"2404.17832","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T08:53:58Z","cross_cats_sorted":[],"title_canon_sha256":"c144ebe2f27dab2043e573488322e744d0f8aed0446b9334a7f03538c98f3a4b","abstract_canon_sha256":"c305d9252239407b85eeda854d3b3cf7c38bb239f38d3014ffe0e1a5724620ce"},"schema_version":"1.0"},"canonical_sha256":"d352e7b230ad1a701ca79dbad17de28bb759b6732f26e75acd021402ea00b067","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:54.722371Z","signature_b64":"HQLwhbJc92SR+Ho3JZ1NASMt5KMCWxN7GgGoP7AXMy/AKZ7J7ZfrYsubpEOTapJL2TWdmeDS3bXwmXRQKCNgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d352e7b230ad1a701ca79dbad17de28bb759b6732f26e75acd021402ea00b067","last_reissued_at":"2026-07-05T08:12:54.721925Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:54.721925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.17832","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":"2026-07-05T08:12:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EplOwArpyrvyKFdzS4VYJ4paly5BUAL9Y4bkSFWCk4IU89QuRH/a9LupoSLZFx8rRti8fhR/L2Ll6pNMb3DDDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:31:56.300271Z"},"content_sha256":"475a2710cf38191f3ba1e8a2aefa2b164b6c9327a8a9f6fd09039e6fbb203e68","schema_version":"1.0","event_id":"sha256:475a2710cf38191f3ba1e8a2aefa2b164b6c9327a8a9f6fd09039e6fbb203e68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2NJOPMRQVUNHAHFHTW5NC7PCRO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dariusz Kajtoch, Tsimur Hadeliya","submitted_at":"2024-04-27T08:53:58Z","abstract_excerpt":"We introduce a few-shot benchmark consisting of 7 different classification tasks native to the Polish language. We conducted an empirical comparison with 0 and 16 shots between fine-tuning, linear probing, SetFit, and in-context learning (ICL) using various pre-trained commercial and open-source models. Our findings reveal that ICL achieves the best performance, with commercial models like GPT-3.5 and GPT-4 attaining the best performance. However, there remains a significant 14 percentage points gap between our best few-shot learning score and the performance of HerBERT-large fine-tuned on the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.17832","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/2404.17832/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":"2026-07-05T08:12:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BOamrn82dcr1T9tOqmzOToKh0tMpYwQ7C7jO1AsinyZKqH8NAFC0EyJ3UZL1guvYqy8w98CeShgZw/l4UfUQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:31:56.300777Z"},"content_sha256":"392b3d18ff1f18e291c4f7c751dffdf28aa201120e01e17d0a057f6351240747","schema_version":"1.0","event_id":"sha256:392b3d18ff1f18e291c4f7c751dffdf28aa201120e01e17d0a057f6351240747"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/bundle.json","state_url":"https://pith.science/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/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":"2026-08-07T17:31:56Z","links":{"resolver":"https://pith.science/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO","bundle":"https://pith.science/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/bundle.json","state":"https://pith.science/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2NJOPMRQVUNHAHFHTW5NC7PCRO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2NJOPMRQVUNHAHFHTW5NC7PCRO","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":"c305d9252239407b85eeda854d3b3cf7c38bb239f38d3014ffe0e1a5724620ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T08:53:58Z","title_canon_sha256":"c144ebe2f27dab2043e573488322e744d0f8aed0446b9334a7f03538c98f3a4b"},"schema_version":"1.0","source":{"id":"2404.17832","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.17832","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"arxiv_version","alias_value":"2404.17832v1","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.17832","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_12","alias_value":"2NJOPMRQVUNH","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_16","alias_value":"2NJOPMRQVUNHAHFH","created_at":"2026-07-05T08:12:54Z"},{"alias_kind":"pith_short_8","alias_value":"2NJOPMRQ","created_at":"2026-07-05T08:12:54Z"}],"graph_snapshots":[{"event_id":"sha256:392b3d18ff1f18e291c4f7c751dffdf28aa201120e01e17d0a057f6351240747","target":"graph","created_at":"2026-07-05T08:12:54Z","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/2404.17832/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a few-shot benchmark consisting of 7 different classification tasks native to the Polish language. We conducted an empirical comparison with 0 and 16 shots between fine-tuning, linear probing, SetFit, and in-context learning (ICL) using various pre-trained commercial and open-source models. Our findings reveal that ICL achieves the best performance, with commercial models like GPT-3.5 and GPT-4 attaining the best performance. However, there remains a significant 14 percentage points gap between our best few-shot learning score and the performance of HerBERT-large fine-tuned on the","authors_text":"Dariusz Kajtoch, Tsimur Hadeliya","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T08:53:58Z","title":"Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.17832","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":"sha256:475a2710cf38191f3ba1e8a2aefa2b164b6c9327a8a9f6fd09039e6fbb203e68","target":"record","created_at":"2026-07-05T08:12:54Z","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":"c305d9252239407b85eeda854d3b3cf7c38bb239f38d3014ffe0e1a5724620ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T08:53:58Z","title_canon_sha256":"c144ebe2f27dab2043e573488322e744d0f8aed0446b9334a7f03538c98f3a4b"},"schema_version":"1.0","source":{"id":"2404.17832","kind":"arxiv","version":1}},"canonical_sha256":"d352e7b230ad1a701ca79dbad17de28bb759b6732f26e75acd021402ea00b067","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d352e7b230ad1a701ca79dbad17de28bb759b6732f26e75acd021402ea00b067","first_computed_at":"2026-07-05T08:12:54.721925Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:12:54.721925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HQLwhbJc92SR+Ho3JZ1NASMt5KMCWxN7GgGoP7AXMy/AKZ7J7ZfrYsubpEOTapJL2TWdmeDS3bXwmXRQKCNgCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:12:54.722371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.17832","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:475a2710cf38191f3ba1e8a2aefa2b164b6c9327a8a9f6fd09039e6fbb203e68","sha256:392b3d18ff1f18e291c4f7c751dffdf28aa201120e01e17d0a057f6351240747"],"state_sha256":"1e90935364f74eeb3a7137b3711113903877eca41c764730830c2e50f9605db2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GU/34xSeFRlE7xk3UzW2OK6A4gfp9IW7exmsFIWBJyvvK7z/bGkFNtT1pk3vbA4iIKSXqYW8quU1hj0QjSXyCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:31:56.305674Z","bundle_sha256":"e38d6b9de1ac28c3e975ca1645391fd10139570c86c65956d4c63697341dde65"}}