{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XFXMLCHVAQXFKIAV3BOV5OOTIA","short_pith_number":"pith:XFXMLCHV","canonical_record":{"source":{"id":"2606.29614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-28T21:41:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e083519bd06a5fe46e00491482b6568056ec4c18c254f092ef2a2ebf4e9a0b2","abstract_canon_sha256":"9eb78277f7bf3326fb1c75481c927893f74731bf4bd5761ef9f67783015e7767"},"schema_version":"1.0"},"canonical_sha256":"b96ec588f5042e552015d85d5eb9d3402a48cc5a0bb8b9065cb5f767ad56c049","source":{"kind":"arxiv","id":"2606.29614","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29614","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29614v1","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29614","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_12","alias_value":"XFXMLCHVAQXF","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_16","alias_value":"XFXMLCHVAQXFKIAV","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_8","alias_value":"XFXMLCHV","created_at":"2026-06-30T01:18:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XFXMLCHVAQXFKIAV3BOV5OOTIA","target":"record","payload":{"canonical_record":{"source":{"id":"2606.29614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-28T21:41:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e083519bd06a5fe46e00491482b6568056ec4c18c254f092ef2a2ebf4e9a0b2","abstract_canon_sha256":"9eb78277f7bf3326fb1c75481c927893f74731bf4bd5761ef9f67783015e7767"},"schema_version":"1.0"},"canonical_sha256":"b96ec588f5042e552015d85d5eb9d3402a48cc5a0bb8b9065cb5f767ad56c049","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:18:19.531722Z","signature_b64":"YciXxj74Z3yk+YeaDeJhUmw54oEuybDTU41r14Q3GWQxaJOztc1eI0or7uX0Y3bvv1SKvOJDhZMMK3wTes2+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b96ec588f5042e552015d85d5eb9d3402a48cc5a0bb8b9065cb5f767ad56c049","last_reissued_at":"2026-06-30T01:18:19.531023Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:18:19.531023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.29614","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:18:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bWGiU6sPqKj9sMw3iELmlihYBYT5K/a33zjiOJyDWXXX4cuznhgUL2j53GAiiIzIayPgg0+rOO6gIX3XGRuiDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T08:35:09.803679Z"},"content_sha256":"3661c039b51802f1cda45024cb75fb54a6956fb2e5c1d45905d0120fabf9e05f","schema_version":"1.0","event_id":"sha256:3661c039b51802f1cda45024cb75fb54a6956fb2e5c1d45905d0120fabf9e05f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XFXMLCHVAQXFKIAV3BOV5OOTIA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Sercan Karaka\\c{s}, Yusuf \\c{S}im\\c{s}ek","submitted_at":"2026-06-28T21:41:29Z","abstract_excerpt":"This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models. We compare classical machine learning methods, fine-tuned pretrained language models, and prompted large language models on a Turkish e-commerce review dataset with negative, neutral, and positive labels. Fine-tuned BERTurk models perform best overall and outperform all prompted large language models in the full three-class task. The neutral class emerges as the main difficulty: while several large language models are much more competitive in binary positive-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29614","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.29614/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:18:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XIHmq2ygLnyVBNIe8kZscuyOcNTOv382OghElWfOKxW7IshGQaNbKuTL91z2Ywd28zGTZ4JRwONEG66VcWOCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T08:35:09.804066Z"},"content_sha256":"650618fc037f572873f244ca53da98e022608985e0a8a551569ad2a730d1b8c2","schema_version":"1.0","event_id":"sha256:650618fc037f572873f244ca53da98e022608985e0a8a551569ad2a730d1b8c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/bundle.json","state_url":"https://pith.science/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-05T08:35:09Z","links":{"resolver":"https://pith.science/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA","bundle":"https://pith.science/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/bundle.json","state":"https://pith.science/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XFXMLCHVAQXFKIAV3BOV5OOTIA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XFXMLCHVAQXFKIAV3BOV5OOTIA","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":"9eb78277f7bf3326fb1c75481c927893f74731bf4bd5761ef9f67783015e7767","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-28T21:41:29Z","title_canon_sha256":"6e083519bd06a5fe46e00491482b6568056ec4c18c254f092ef2a2ebf4e9a0b2"},"schema_version":"1.0","source":{"id":"2606.29614","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29614","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29614v1","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29614","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_12","alias_value":"XFXMLCHVAQXF","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_16","alias_value":"XFXMLCHVAQXFKIAV","created_at":"2026-06-30T01:18:19Z"},{"alias_kind":"pith_short_8","alias_value":"XFXMLCHV","created_at":"2026-06-30T01:18:19Z"}],"graph_snapshots":[{"event_id":"sha256:650618fc037f572873f244ca53da98e022608985e0a8a551569ad2a730d1b8c2","target":"graph","created_at":"2026-06-30T01:18:19Z","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/2606.29614/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models. We compare classical machine learning methods, fine-tuned pretrained language models, and prompted large language models on a Turkish e-commerce review dataset with negative, neutral, and positive labels. Fine-tuned BERTurk models perform best overall and outperform all prompted large language models in the full three-class task. The neutral class emerges as the main difficulty: while several large language models are much more competitive in binary positive-","authors_text":"Sercan Karaka\\c{s}, Yusuf \\c{S}im\\c{s}ek","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-28T21:41:29Z","title":"Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29614","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":"sha256:3661c039b51802f1cda45024cb75fb54a6956fb2e5c1d45905d0120fabf9e05f","target":"record","created_at":"2026-06-30T01:18:19Z","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":"9eb78277f7bf3326fb1c75481c927893f74731bf4bd5761ef9f67783015e7767","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-28T21:41:29Z","title_canon_sha256":"6e083519bd06a5fe46e00491482b6568056ec4c18c254f092ef2a2ebf4e9a0b2"},"schema_version":"1.0","source":{"id":"2606.29614","kind":"arxiv","version":1}},"canonical_sha256":"b96ec588f5042e552015d85d5eb9d3402a48cc5a0bb8b9065cb5f767ad56c049","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b96ec588f5042e552015d85d5eb9d3402a48cc5a0bb8b9065cb5f767ad56c049","first_computed_at":"2026-06-30T01:18:19.531023Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:18:19.531023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YciXxj74Z3yk+YeaDeJhUmw54oEuybDTU41r14Q3GWQxaJOztc1eI0or7uX0Y3bvv1SKvOJDhZMMK3wTes2+CQ==","signature_status":"signed_v1","signed_at":"2026-06-30T01:18:19.531722Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29614","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3661c039b51802f1cda45024cb75fb54a6956fb2e5c1d45905d0120fabf9e05f","sha256:650618fc037f572873f244ca53da98e022608985e0a8a551569ad2a730d1b8c2"],"state_sha256":"6ed8f3760892f743566bdfa7d231fdf25309469afd5d331be594c14b58c35cb1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N2hdEwtNlruYV+u1eJ/rcD//MVkXhEe2liMyK0HBNQc9OTP0+LUYMjmpX6s0yWEpDfPHu1PK30g7aC+3Hqd+AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T08:35:09.806032Z","bundle_sha256":"f25cf2ea2801c93a54e53fff4deb3298203360c7e498559595e480703558f73e"}}