{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:AY6KSZCMBVMYVTG3FU4WG7GPFI","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":"d50bb086b87fc7d54cca238623b4b3398b5ce4d574fbdc870bd1a24aca5d5fda","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-21T12:16:56Z","title_canon_sha256":"b81e9c46667cd18be400bde3d5653967453a2f7ff3e07ff05a5a5e79e26fc9c2"},"schema_version":"1.0","source":{"id":"2109.10126","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.10126","created_at":"2026-07-05T03:15:58Z"},{"alias_kind":"arxiv_version","alias_value":"2109.10126v1","created_at":"2026-07-05T03:15:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.10126","created_at":"2026-07-05T03:15:58Z"},{"alias_kind":"pith_short_12","alias_value":"AY6KSZCMBVMY","created_at":"2026-07-05T03:15:58Z"},{"alias_kind":"pith_short_16","alias_value":"AY6KSZCMBVMYVTG3","created_at":"2026-07-05T03:15:58Z"},{"alias_kind":"pith_short_8","alias_value":"AY6KSZCM","created_at":"2026-07-05T03:15:58Z"}],"graph_snapshots":[{"event_id":"sha256:65da90709715d14de2485646c4606ac780ba372265617e7ab7d99512e7518d1f","target":"graph","created_at":"2026-07-05T03:15:58Z","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/2109.10126/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conversational tasks such as intent detection (ID). In this work, we propose ConvFiT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 ConvFiT-ing) and task-specialised sentence encoder (after Stage 2). We d","authors_text":"Daniela Gerz, I\\~nigo Casanueva, Ivan Vuli\\'c, Nikola Mrk\\v{s}i\\'c, Pawe{\\l} Budzianowski, Pei-Hao Su, Sam Coope, Tsung-Hsien Wen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-21T12:16:56Z","title":"ConvFiT: Conversational Fine-Tuning of Pretrained Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.10126","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":"sha256:08007f5106beef92912d15a1ef94eb9924fbfb6630fd9bca5d54bf7f1663f4fe","target":"record","created_at":"2026-07-05T03:15:58Z","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":"d50bb086b87fc7d54cca238623b4b3398b5ce4d574fbdc870bd1a24aca5d5fda","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-21T12:16:56Z","title_canon_sha256":"b81e9c46667cd18be400bde3d5653967453a2f7ff3e07ff05a5a5e79e26fc9c2"},"schema_version":"1.0","source":{"id":"2109.10126","kind":"arxiv","version":1}},"canonical_sha256":"063ca9644c0d598accdb2d39637ccf2a1eeabcfa4f3213c9179da5f12cb60619","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"063ca9644c0d598accdb2d39637ccf2a1eeabcfa4f3213c9179da5f12cb60619","first_computed_at":"2026-07-05T03:15:58.433261Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:15:58.433261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fc9BCdLz84c+HGGhhtyrajqvqcF5WD9fPRMgUMCMYIWGgFUoozQq25NhDghXrTJZaQ09mWfwMCxSP5OWhAnlAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:15:58.433683Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.10126","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08007f5106beef92912d15a1ef94eb9924fbfb6630fd9bca5d54bf7f1663f4fe","sha256:65da90709715d14de2485646c4606ac780ba372265617e7ab7d99512e7518d1f"],"state_sha256":"5c259dcc979bffab6a28b2b7f5db1a3c4d4365466d366c405a196c19be75fa2b"}