{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JZ4BQYV5V56UYRGGSSMOZ5MQLP","short_pith_number":"pith:JZ4BQYV5","schema_version":"1.0","canonical_sha256":"4e781862bdaf7d4c44c69498ecf5905bfa3c7d609ce8d6609edc80a69f8c7f38","source":{"kind":"arxiv","id":"2403.17661","version":2},"attestation_state":"computed","paper":{"title":"Language Models for Text Classification: Is In-Context Learning Enough?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aleksandra Edwards, Jose Camacho-Collados","submitted_at":"2024-03-26T12:47:39Z","abstract_excerpt":"Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches based on fine-tuning is the ability to understand instructions written in natural language (prompts), which helps them generalise better to different tasks and domains without the need for specific training data. This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances. However, existing research is limited in scale and lacks understanding of ho"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2403.17661","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-26T12:47:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"21c1cc3359425226240a759075a402a4399f231c6b49f3c5f3a030cbe6d99d38","abstract_canon_sha256":"bfa3106a4e5556a25627ca70e43215a4706dee8b39edf05fff41f692e88c742d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:34.913994Z","signature_b64":"70MAqkhc0w0L/C47zsO6PlqPLbD2ubEtMGFvZeUQr1xKcVSf1RifCePVJ51S0PDoSmqe6Ef9IKDbPpFTFDBfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e781862bdaf7d4c44c69498ecf5905bfa3c7d609ce8d6609edc80a69f8c7f38","last_reissued_at":"2026-07-05T08:07:34.913534Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:34.913534Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language Models for Text Classification: Is In-Context Learning Enough?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aleksandra Edwards, Jose Camacho-Collados","submitted_at":"2024-03-26T12:47:39Z","abstract_excerpt":"Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches based on fine-tuning is the ability to understand instructions written in natural language (prompts), which helps them generalise better to different tasks and domains without the need for specific training data. This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances. However, existing research is limited in scale and lacks understanding of ho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.17661","kind":"arxiv","version":2},"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/2403.17661/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2403.17661","created_at":"2026-07-05T08:07:34.913588+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.17661v2","created_at":"2026-07-05T08:07:34.913588+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.17661","created_at":"2026-07-05T08:07:34.913588+00:00"},{"alias_kind":"pith_short_12","alias_value":"JZ4BQYV5V56U","created_at":"2026-07-05T08:07:34.913588+00:00"},{"alias_kind":"pith_short_16","alias_value":"JZ4BQYV5V56UYRGG","created_at":"2026-07-05T08:07:34.913588+00:00"},{"alias_kind":"pith_short_8","alias_value":"JZ4BQYV5","created_at":"2026-07-05T08:07:34.913588+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.05525","citing_title":"Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP","json":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP.json","graph_json":"https://pith.science/api/pith-number/JZ4BQYV5V56UYRGGSSMOZ5MQLP/graph.json","events_json":"https://pith.science/api/pith-number/JZ4BQYV5V56UYRGGSSMOZ5MQLP/events.json","paper":"https://pith.science/paper/JZ4BQYV5"},"agent_actions":{"view_html":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP","download_json":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP.json","view_paper":"https://pith.science/paper/JZ4BQYV5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.17661&json=true","fetch_graph":"https://pith.science/api/pith-number/JZ4BQYV5V56UYRGGSSMOZ5MQLP/graph.json","fetch_events":"https://pith.science/api/pith-number/JZ4BQYV5V56UYRGGSSMOZ5MQLP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP/action/storage_attestation","attest_author":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP/action/author_attestation","sign_citation":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP/action/citation_signature","submit_replication":"https://pith.science/pith/JZ4BQYV5V56UYRGGSSMOZ5MQLP/action/replication_record"}},"created_at":"2026-07-05T08:07:34.913588+00:00","updated_at":"2026-07-05T08:07:34.913588+00:00"}