{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6QYN4KXG5ACS4UHKQRGIE2RMGH","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":"61d51b02cac1a9cec984aacb40d0fb13fb75a81bcf1421d45fd670be51b5139e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T16:51:02Z","title_canon_sha256":"7ee72bdc6bfd7914c95d1cd5da372da4f4a87244eb42ef69e6d948f0ce4e3e7c"},"schema_version":"1.0","source":{"id":"2407.09424","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.09424","created_at":"2026-07-05T08:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.09424v1","created_at":"2026-07-05T08:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.09424","created_at":"2026-07-05T08:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"6QYN4KXG5ACS","created_at":"2026-07-05T08:43:18Z"},{"alias_kind":"pith_short_16","alias_value":"6QYN4KXG5ACS4UHK","created_at":"2026-07-05T08:43:18Z"},{"alias_kind":"pith_short_8","alias_value":"6QYN4KXG","created_at":"2026-07-05T08:43:18Z"}],"graph_snapshots":[{"event_id":"sha256:c679b746ccd56ed75c896072937f5102555c15ec8b6de385004350323b55e463","target":"graph","created_at":"2026-07-05T08:43:18Z","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/2407.09424/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized knowledge in telecom domain. In this paper, for the first time, we propose a pipeline to adapt any general purpose LLMs to a telecom-specific LLMs. We collect and build telecom-specific pre-train dataset, instruction dataset, preference dataset to perform continual pre-training, instruct tuning and alignment tuning respectively. Besides, due to the lack of widely accepted evaluation benchmarks in telecom domain, we ex","authors_text":"Faouzi Bader, Hang Zou, Lina Bariah, Merouane Debbah, Qiyang Zhao, Thierry Lestable, Yu Tian","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T16:51:02Z","title":"TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.09424","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:ab2d47bcdf30902b0a135c9af233d2e78d61be27bb2f9294eba3bf93f8800e8e","target":"record","created_at":"2026-07-05T08:43:18Z","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":"61d51b02cac1a9cec984aacb40d0fb13fb75a81bcf1421d45fd670be51b5139e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T16:51:02Z","title_canon_sha256":"7ee72bdc6bfd7914c95d1cd5da372da4f4a87244eb42ef69e6d948f0ce4e3e7c"},"schema_version":"1.0","source":{"id":"2407.09424","kind":"arxiv","version":1}},"canonical_sha256":"f430de2ae6e8052e50ea844c826a2c31d67a9349a482aa8d9187978604211196","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f430de2ae6e8052e50ea844c826a2c31d67a9349a482aa8d9187978604211196","first_computed_at":"2026-07-05T08:43:18.940142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:43:18.940142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CHbUaeDDN2KW+gKUWiIbvHbBjRXlxPJP5HutEbRA8zBW2+L1c6Hrx830RCp/lVHqEvtYuCu2K8L7vckVqqffAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:43:18.940575Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.09424","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab2d47bcdf30902b0a135c9af233d2e78d61be27bb2f9294eba3bf93f8800e8e","sha256:c679b746ccd56ed75c896072937f5102555c15ec8b6de385004350323b55e463"],"state_sha256":"34c3cba549edd8c7daa0f062d1fbf0df17b058ea7aff1c2985a49f0cb97013ea"}