{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7XNH6EOMQBN624FOS6DC3AY7FW","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":"6e2d886cb02fe0ff93c2efe8956f153265674097262b0c36609aef87148ac0de","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T16:35:55Z","title_canon_sha256":"d7061c26713ca9c88a600734c8120a7890d4edeb470cc318e0e868b0634ece2b"},"schema_version":"1.0","source":{"id":"2310.19708","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.19708","created_at":"2026-07-05T07:07:40Z"},{"alias_kind":"arxiv_version","alias_value":"2310.19708v3","created_at":"2026-07-05T07:07:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19708","created_at":"2026-07-05T07:07:40Z"},{"alias_kind":"pith_short_12","alias_value":"7XNH6EOMQBN6","created_at":"2026-07-05T07:07:40Z"},{"alias_kind":"pith_short_16","alias_value":"7XNH6EOMQBN624FO","created_at":"2026-07-05T07:07:40Z"},{"alias_kind":"pith_short_8","alias_value":"7XNH6EOM","created_at":"2026-07-05T07:07:40Z"}],"graph_snapshots":[{"event_id":"sha256:c9d4e57629e0b30bedae5125d01f4e77a05cf02882f18bb8f668cd3e102a9eb4","target":"graph","created_at":"2026-07-05T07:07:40Z","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/2310.19708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"General purpose language models (LMs) encounter difficulties when processing domain-specific jargon and terminology, which are frequently utilized in specialized fields such as medicine or industrial settings. Moreover, they often find it challenging to interpret mixed speech that blends general language with specialized jargon. This poses a challenge for automatic speech recognition systems operating within these specific domains. In this work, we introduce a novel approach that integrates domain-specific or secondary LM into general-purpose LM. This strategy involves labeling, or \"coloring\",","authors_text":"Aviv Navon, Aviv Shamsian, Daniel Eitan, Gidon Krendel, Gil Ayach, Gil Hetz, Joseph Keshet, Menachem Pirchi, Neta Glazer, Shai Meital","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T16:35:55Z","title":"Combining Language Models For Specialized Domains: A Colorful Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19708","kind":"arxiv","version":3},"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":"sha256:98a0915371487d957cb31a93022a9fa1b581ccb648d927d0ab8d30ba523a601c","target":"record","created_at":"2026-07-05T07:07:40Z","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":"6e2d886cb02fe0ff93c2efe8956f153265674097262b0c36609aef87148ac0de","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T16:35:55Z","title_canon_sha256":"d7061c26713ca9c88a600734c8120a7890d4edeb470cc318e0e868b0634ece2b"},"schema_version":"1.0","source":{"id":"2310.19708","kind":"arxiv","version":3}},"canonical_sha256":"fdda7f11cc805bed70ae97862d831f2d890d8092f7c3981f5c32442b8949dbdd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fdda7f11cc805bed70ae97862d831f2d890d8092f7c3981f5c32442b8949dbdd","first_computed_at":"2026-07-05T07:07:40.496681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:40.496681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5ZDYX8+ixFvaoSYi/sKJJtj13Zks9aM6OLKtSP2fzkDk+o5Euws5L4Cw7q4eMSe0rwOCHa0ySO/IA8VqDuyfAw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:40.497088Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.19708","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98a0915371487d957cb31a93022a9fa1b581ccb648d927d0ab8d30ba523a601c","sha256:c9d4e57629e0b30bedae5125d01f4e77a05cf02882f18bb8f668cd3e102a9eb4"],"state_sha256":"c51f045e2657db48347ed1d6a26231563d888c93257069cb4dde1d50f5c20cf0"}