{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BRRQMRQFKVY47VTYL4KLHOSMRG","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":"ba28ef808b2ce566057f6f038bdd006b537320bffdbc7d65c3ae2dc6c89e7452","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-23T16:23:17Z","title_canon_sha256":"79be93da7c1480dc3e3c7dae34760865acb57ae8b14f2a6d48339532669a5d1d"},"schema_version":"1.0","source":{"id":"2504.16856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.16856","created_at":"2026-07-05T10:53:05Z"},{"alias_kind":"arxiv_version","alias_value":"2504.16856v1","created_at":"2026-07-05T10:53:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16856","created_at":"2026-07-05T10:53:05Z"},{"alias_kind":"pith_short_12","alias_value":"BRRQMRQFKVY4","created_at":"2026-07-05T10:53:05Z"},{"alias_kind":"pith_short_16","alias_value":"BRRQMRQFKVY47VTY","created_at":"2026-07-05T10:53:05Z"},{"alias_kind":"pith_short_8","alias_value":"BRRQMRQF","created_at":"2026-07-05T10:53:05Z"}],"graph_snapshots":[{"event_id":"sha256:d47851104a3561cb45b2889b8e1db784df84997e8be7a139adbf5c40ebdce6d1","target":"graph","created_at":"2026-07-05T10:53:05Z","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/2504.16856/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) like GPT-4 suffer from over-predicting emotions and are too resource-intensive. We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models. We focus on enlarging the semantic diversity of examples and propose grounding the generation into a corpus of narra","authors_text":"Alexander Shvets","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-23T16:23:17Z","title":"Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16856","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:bec3fcbf85bd3ee8e8472628eb4a0881f4c9725ebbcc28ccd39e910c442cd887","target":"record","created_at":"2026-07-05T10:53:05Z","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":"ba28ef808b2ce566057f6f038bdd006b537320bffdbc7d65c3ae2dc6c89e7452","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-23T16:23:17Z","title_canon_sha256":"79be93da7c1480dc3e3c7dae34760865acb57ae8b14f2a6d48339532669a5d1d"},"schema_version":"1.0","source":{"id":"2504.16856","kind":"arxiv","version":1}},"canonical_sha256":"0c630646055571cfd6785f14b3ba4c89a58a58204de9d97208101ac597284017","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c630646055571cfd6785f14b3ba4c89a58a58204de9d97208101ac597284017","first_computed_at":"2026-07-05T10:53:05.516566Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:53:05.516566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ah20MsemCerE0EXrny3cXI7t31byaFg5kMmcvhyR36tCLurtGP8BCWAohlaYoVsmcj6/gsib/X4QPRGLH1RnBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:53:05.517061Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.16856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bec3fcbf85bd3ee8e8472628eb4a0881f4c9725ebbcc28ccd39e910c442cd887","sha256:d47851104a3561cb45b2889b8e1db784df84997e8be7a139adbf5c40ebdce6d1"],"state_sha256":"0139ffeef70507c3e89f8326423d4173eea9c7223c987c276e49b03425b4d4de"}