{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:F4Y7Y7OTFCJR2BRG44P7QSGRHW","short_pith_number":"pith:F4Y7Y7OT","schema_version":"1.0","canonical_sha256":"2f31fc7dd328931d0626e71ff848d13d82213afb2cf84084828bd597331b33f6","source":{"kind":"arxiv","id":"1811.00942","version":1},"attestation_state":"computed","paper":{"title":"Progress and Tradeoffs in Neural Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jimmy Lin, Raphael Tang","submitted_at":"2018-11-02T15:46:52Z","abstract_excerpt":"In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality-performance tradeoff of various language modeling techniques, represents to our knowledge the first to make this observation. We compare state-of-the-art NLM"},"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":"1811.00942","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-02T15:46:52Z","cross_cats_sorted":[],"title_canon_sha256":"5bc07221834eabb18c8ebbc595a4a6469766a2ed5854f83accde362919497d84","abstract_canon_sha256":"767a46859ff4198bd17cf03e0b8d4ea78deac7e7208792bd7c105da86253c91e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:41.666983Z","signature_b64":"t+8PaBaiCTU21YrCbbNNWIJEG/Ta6B9TwLFfX2P1Vmwisn1A8JcZrXyyDb5FUQaELf0CQejfa4lQW6FK9lYRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f31fc7dd328931d0626e71ff848d13d82213afb2cf84084828bd597331b33f6","last_reissued_at":"2026-05-18T00:01:41.666309Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:41.666309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progress and Tradeoffs in Neural Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jimmy Lin, Raphael Tang","submitted_at":"2018-11-02T15:46:52Z","abstract_excerpt":"In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality-performance tradeoff of various language modeling techniques, represents to our knowledge the first to make this observation. We compare state-of-the-art NLM"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00942","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":""},"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":"1811.00942","created_at":"2026-05-18T00:01:41.666403+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00942v1","created_at":"2026-05-18T00:01:41.666403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00942","created_at":"2026-05-18T00:01:41.666403+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4Y7Y7OTFCJR","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4Y7Y7OTFCJR2BRG","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4Y7Y7OT","created_at":"2026-05-18T12:32:22.470017+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW","json":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW.json","graph_json":"https://pith.science/api/pith-number/F4Y7Y7OTFCJR2BRG44P7QSGRHW/graph.json","events_json":"https://pith.science/api/pith-number/F4Y7Y7OTFCJR2BRG44P7QSGRHW/events.json","paper":"https://pith.science/paper/F4Y7Y7OT"},"agent_actions":{"view_html":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW","download_json":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW.json","view_paper":"https://pith.science/paper/F4Y7Y7OT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00942&json=true","fetch_graph":"https://pith.science/api/pith-number/F4Y7Y7OTFCJR2BRG44P7QSGRHW/graph.json","fetch_events":"https://pith.science/api/pith-number/F4Y7Y7OTFCJR2BRG44P7QSGRHW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW/action/storage_attestation","attest_author":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW/action/author_attestation","sign_citation":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW/action/citation_signature","submit_replication":"https://pith.science/pith/F4Y7Y7OTFCJR2BRG44P7QSGRHW/action/replication_record"}},"created_at":"2026-05-18T00:01:41.666403+00:00","updated_at":"2026-05-18T00:01:41.666403+00:00"}