{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3T6NR45GAZ3VZLKLT7PRS4JTM5","short_pith_number":"pith:3T6NR45G","canonical_record":{"source":{"id":"2502.11830","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T14:25:54Z","cross_cats_sorted":[],"title_canon_sha256":"3e5d43aae9840d00e235397271f89b09fc8da83e069a0d275a926e9697778d5f","abstract_canon_sha256":"2a7e4bf19215655c7deb939a05c498dcdce2ca37991c514f0515d2a22a7d0770"},"schema_version":"1.0"},"canonical_sha256":"dcfcd8f3a606775cad4b9fdf197133675353d8d2d1664ab2da56a5a64e96b42b","source":{"kind":"arxiv","id":"2502.11830","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11830","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11830v1","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11830","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_12","alias_value":"3T6NR45GAZ3V","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_16","alias_value":"3T6NR45GAZ3VZLKL","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_8","alias_value":"3T6NR45G","created_at":"2026-07-05T10:17:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3T6NR45GAZ3VZLKLT7PRS4JTM5","target":"record","payload":{"canonical_record":{"source":{"id":"2502.11830","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T14:25:54Z","cross_cats_sorted":[],"title_canon_sha256":"3e5d43aae9840d00e235397271f89b09fc8da83e069a0d275a926e9697778d5f","abstract_canon_sha256":"2a7e4bf19215655c7deb939a05c498dcdce2ca37991c514f0515d2a22a7d0770"},"schema_version":"1.0"},"canonical_sha256":"dcfcd8f3a606775cad4b9fdf197133675353d8d2d1664ab2da56a5a64e96b42b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:17.868062Z","signature_b64":"GDCVigj9MiDy/4s572e8eueaQD4ST1lJTxtmJCAKMsvfVaR1VadwFDHStthdU91eAwN1pMKqK/xv7YUhtAP9DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcfcd8f3a606775cad4b9fdf197133675353d8d2d1664ab2da56a5a64e96b42b","last_reissued_at":"2026-07-05T10:17:17.867544Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:17.867544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.11830","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":"2026-07-05T10:17:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wdrmp/+wy2YltfQarRkmnC3f3DF6ZCZseVI1SJwO/1H83qoEck2uj1oQdGJur/asOCbP+qxE/1I5wc1opmW9Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:01:32.214594Z"},"content_sha256":"eb42e3c3f8d66a0ac390b5fad1a09413c442d4c6fc5610af568985e08420495d","schema_version":"1.0","event_id":"sha256:eb42e3c3f8d66a0ac390b5fad1a09413c442d4c6fc5610af568985e08420495d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3T6NR45GAZ3VZLKLT7PRS4JTM5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Text Classification in the LLM Era -- Where do we stand?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shwetali Shimangaud, Sowmya Vajjala","submitted_at":"2025-02-17T14:25:54Z","abstract_excerpt":"Large Language Models revolutionized NLP and showed dramatic performance improvements across several tasks. In this paper, we investigated the role of such language models in text classification and how they compare with other approaches relying on smaller pre-trained language models. Considering 32 datasets spanning 8 languages, we compared zero-shot classification, few-shot fine-tuning and synthetic data based classifiers with classifiers built using the complete human labeled dataset. Our results show that zero-shot approaches do well for sentiment classification, but are outperformed by ot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11830","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/2502.11830/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":"2026-07-05T10:17:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a/CHOxA5HlT/XY5Q7xL7QQ/MoOVAx3DCHDhXcksOxWFsC1WBXUl8vGFz5BlAPM9TQ59gyZl5hm2i1e7yxIknBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:01:32.214972Z"},"content_sha256":"b84d4742e6fd2bb5bd90afb21871861662a0b38698a1efdb8ee3ba72d7d9e7b5","schema_version":"1.0","event_id":"sha256:b84d4742e6fd2bb5bd90afb21871861662a0b38698a1efdb8ee3ba72d7d9e7b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/bundle.json","state_url":"https://pith.science/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/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":"2026-08-05T00:01:32Z","links":{"resolver":"https://pith.science/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5","bundle":"https://pith.science/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/bundle.json","state":"https://pith.science/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3T6NR45GAZ3VZLKLT7PRS4JTM5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3T6NR45GAZ3VZLKLT7PRS4JTM5","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":"2a7e4bf19215655c7deb939a05c498dcdce2ca37991c514f0515d2a22a7d0770","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T14:25:54Z","title_canon_sha256":"3e5d43aae9840d00e235397271f89b09fc8da83e069a0d275a926e9697778d5f"},"schema_version":"1.0","source":{"id":"2502.11830","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11830","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11830v1","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11830","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_12","alias_value":"3T6NR45GAZ3V","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_16","alias_value":"3T6NR45GAZ3VZLKL","created_at":"2026-07-05T10:17:17Z"},{"alias_kind":"pith_short_8","alias_value":"3T6NR45G","created_at":"2026-07-05T10:17:17Z"}],"graph_snapshots":[{"event_id":"sha256:b84d4742e6fd2bb5bd90afb21871861662a0b38698a1efdb8ee3ba72d7d9e7b5","target":"graph","created_at":"2026-07-05T10:17:17Z","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/2502.11830/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models revolutionized NLP and showed dramatic performance improvements across several tasks. In this paper, we investigated the role of such language models in text classification and how they compare with other approaches relying on smaller pre-trained language models. Considering 32 datasets spanning 8 languages, we compared zero-shot classification, few-shot fine-tuning and synthetic data based classifiers with classifiers built using the complete human labeled dataset. Our results show that zero-shot approaches do well for sentiment classification, but are outperformed by ot","authors_text":"Shwetali Shimangaud, Sowmya Vajjala","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T14:25:54Z","title":"Text Classification in the LLM Era -- Where do we stand?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11830","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:eb42e3c3f8d66a0ac390b5fad1a09413c442d4c6fc5610af568985e08420495d","target":"record","created_at":"2026-07-05T10:17:17Z","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":"2a7e4bf19215655c7deb939a05c498dcdce2ca37991c514f0515d2a22a7d0770","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T14:25:54Z","title_canon_sha256":"3e5d43aae9840d00e235397271f89b09fc8da83e069a0d275a926e9697778d5f"},"schema_version":"1.0","source":{"id":"2502.11830","kind":"arxiv","version":1}},"canonical_sha256":"dcfcd8f3a606775cad4b9fdf197133675353d8d2d1664ab2da56a5a64e96b42b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcfcd8f3a606775cad4b9fdf197133675353d8d2d1664ab2da56a5a64e96b42b","first_computed_at":"2026-07-05T10:17:17.867544Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:17:17.867544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GDCVigj9MiDy/4s572e8eueaQD4ST1lJTxtmJCAKMsvfVaR1VadwFDHStthdU91eAwN1pMKqK/xv7YUhtAP9DA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:17:17.868062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11830","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb42e3c3f8d66a0ac390b5fad1a09413c442d4c6fc5610af568985e08420495d","sha256:b84d4742e6fd2bb5bd90afb21871861662a0b38698a1efdb8ee3ba72d7d9e7b5"],"state_sha256":"5c0a61bb87e2e52e777143859efaad61d954e7b1b45f620e35d33810522b55c9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WNril9EHkpkvFDNjOS8b449oOguOhteCtcxpQnzWxYTBcYT2eHFMW5vdaYO7ih8oxZoWT1lkLjSIy8Z42wM2AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T00:01:32.218111Z","bundle_sha256":"579dd1c42e8c0b6f1fdc89f3ec82a0e6bf4d58b765ab5bb19edd51ac875a3460"}}