{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OW5PV6QXHYGDX3XNU2TC3OCHOT","short_pith_number":"pith:OW5PV6QX","canonical_record":{"source":{"id":"2404.02422","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-03T03:24:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c8952e680d86073c19f9d744fdf32f31fb1ca9607e777946f0cbfe5e504866eb","abstract_canon_sha256":"202a68c3a1d69c462277fbd8fa722677acddef913af8a3701cfd4dbb0629bb74"},"schema_version":"1.0"},"canonical_sha256":"75bafafa173e0c3beeeda6a62db84774d440cf0ef479f032301c36e75fce87af","source":{"kind":"arxiv","id":"2404.02422","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.02422","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2404.02422v1","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.02422","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"OW5PV6QXHYGD","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"OW5PV6QXHYGDX3XN","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"OW5PV6QX","created_at":"2026-07-05T08:03:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OW5PV6QXHYGDX3XNU2TC3OCHOT","target":"record","payload":{"canonical_record":{"source":{"id":"2404.02422","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-03T03:24:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c8952e680d86073c19f9d744fdf32f31fb1ca9607e777946f0cbfe5e504866eb","abstract_canon_sha256":"202a68c3a1d69c462277fbd8fa722677acddef913af8a3701cfd4dbb0629bb74"},"schema_version":"1.0"},"canonical_sha256":"75bafafa173e0c3beeeda6a62db84774d440cf0ef479f032301c36e75fce87af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:56.054114Z","signature_b64":"zJfv2q0sS3/MfDvAoMLh6TB/e/iOhFEv5OqdffG5PnDwLzom6DrbZH8JXyIcAm3uPo0PJmTRj2uzi07+myMiCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75bafafa173e0c3beeeda6a62db84774d440cf0ef479f032301c36e75fce87af","last_reissued_at":"2026-07-05T08:03:56.053684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:56.053684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.02422","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":"2026-07-05T08:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b4jvoRgxOdK3oLHiOAgdz9RoAzgulacjIWS6eklVjwBBq+d3toDemwM34P3/N61DoldwWryO7szYZ9Eu/LajCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:19:50.054610Z"},"content_sha256":"f1355791c0b984a8b001fae5d5eb880a486ef162f42de171d1bc9ed9ff6a991a","schema_version":"1.0","event_id":"sha256:f1355791c0b984a8b001fae5d5eb880a486ef162f42de171d1bc9ed9ff6a991a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OW5PV6QXHYGDX3XNU2TC3OCHOT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Giuseppe Castellucci, Oleg Rokhlenko, Parth Patwa, Shervin Malmasi, Simone Filice, Zhiyu Chen","submitted_at":"2024-04-03T03:24:19Z","abstract_excerpt":"Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt. In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL. Our solution targets the low resource setting, i.e., when only 4 examples per class are available. Using a single LLM and few-shot real data we perform a sequence of generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.02422","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/2404.02422/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":"2026-07-05T08:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NxSWikkZwHuN0UuFiv1t4YPCE7kJvmcparlb1gxmBAdNvxXi+oWap2Mr9z2x2Cv3Sb5UaqKLqErvka8Jyc6+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:19:50.055543Z"},"content_sha256":"461faa7f74e81bfbba9288b35af699b06281b42df99c4467ac629179c45f2465","schema_version":"1.0","event_id":"sha256:461faa7f74e81bfbba9288b35af699b06281b42df99c4467ac629179c45f2465"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/bundle.json","state_url":"https://pith.science/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/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":"2026-08-15T21:19:50Z","links":{"resolver":"https://pith.science/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT","bundle":"https://pith.science/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/bundle.json","state":"https://pith.science/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OW5PV6QXHYGDX3XNU2TC3OCHOT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OW5PV6QXHYGDX3XNU2TC3OCHOT","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":"202a68c3a1d69c462277fbd8fa722677acddef913af8a3701cfd4dbb0629bb74","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-03T03:24:19Z","title_canon_sha256":"c8952e680d86073c19f9d744fdf32f31fb1ca9607e777946f0cbfe5e504866eb"},"schema_version":"1.0","source":{"id":"2404.02422","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.02422","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2404.02422v1","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.02422","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"OW5PV6QXHYGD","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"OW5PV6QXHYGDX3XN","created_at":"2026-07-05T08:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"OW5PV6QX","created_at":"2026-07-05T08:03:56Z"}],"graph_snapshots":[{"event_id":"sha256:461faa7f74e81bfbba9288b35af699b06281b42df99c4467ac629179c45f2465","target":"graph","created_at":"2026-07-05T08:03:56Z","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/2404.02422/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt. In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL. Our solution targets the low resource setting, i.e., when only 4 examples per class are available. Using a single LLM and few-shot real data we perform a sequence of generat","authors_text":"Giuseppe Castellucci, Oleg Rokhlenko, Parth Patwa, Shervin Malmasi, Simone Filice, Zhiyu Chen","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-03T03:24:19Z","title":"Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.02422","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:f1355791c0b984a8b001fae5d5eb880a486ef162f42de171d1bc9ed9ff6a991a","target":"record","created_at":"2026-07-05T08:03:56Z","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":"202a68c3a1d69c462277fbd8fa722677acddef913af8a3701cfd4dbb0629bb74","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-03T03:24:19Z","title_canon_sha256":"c8952e680d86073c19f9d744fdf32f31fb1ca9607e777946f0cbfe5e504866eb"},"schema_version":"1.0","source":{"id":"2404.02422","kind":"arxiv","version":1}},"canonical_sha256":"75bafafa173e0c3beeeda6a62db84774d440cf0ef479f032301c36e75fce87af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75bafafa173e0c3beeeda6a62db84774d440cf0ef479f032301c36e75fce87af","first_computed_at":"2026-07-05T08:03:56.053684Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:56.053684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zJfv2q0sS3/MfDvAoMLh6TB/e/iOhFEv5OqdffG5PnDwLzom6DrbZH8JXyIcAm3uPo0PJmTRj2uzi07+myMiCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:56.054114Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.02422","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1355791c0b984a8b001fae5d5eb880a486ef162f42de171d1bc9ed9ff6a991a","sha256:461faa7f74e81bfbba9288b35af699b06281b42df99c4467ac629179c45f2465"],"state_sha256":"53608d7769957b9fa48d893d71bb5047a2518a146590a49548dce37fe7ec109b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iAIo1vjhxYKgRySyJwf7cHibWX5iqrnqCTWHSMT3fi26ToAcBWySs5ePNwuJ6oSB6S96paiiOX4KU/21DAhbAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T21:19:50.061681Z","bundle_sha256":"f1cce3d38affd672b9c4b420e2758197da050df0fa56bf02c1d0f9f0c3512b7e"}}