{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HJWTH2OCKG5HBQJ5Q6YKIG6ZQ7","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":"0de43731dd0009087701b69ef32899220963b54e3255e8fd21f7c21045679bd3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T10:26:54Z","title_canon_sha256":"96e2f94acdd1fe3c2f7191f0fe3803794685bb38d9ff16b2c9044e2156af0065"},"schema_version":"1.0","source":{"id":"2412.12761","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12761","created_at":"2026-07-05T12:06:04Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12761v2","created_at":"2026-07-05T12:06:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12761","created_at":"2026-07-05T12:06:04Z"},{"alias_kind":"pith_short_12","alias_value":"HJWTH2OCKG5H","created_at":"2026-07-05T12:06:04Z"},{"alias_kind":"pith_short_16","alias_value":"HJWTH2OCKG5HBQJ5","created_at":"2026-07-05T12:06:04Z"},{"alias_kind":"pith_short_8","alias_value":"HJWTH2OC","created_at":"2026-07-05T12:06:04Z"}],"graph_snapshots":[{"event_id":"sha256:1aea20db723065fcaa1d595abbb85c060889980d1352e7b5e06ada9bef44bc66","target":"graph","created_at":"2026-07-05T12:06:04Z","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/2412.12761/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. Particularly, we tried three approaches: (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting and instruction finetuning very large multilingual language models (VMLMs). In native sample mixing, we added monolingual task samples to code-mixed training sets. In MTL learning, we relied on native and code-mixed samples of a semantically related task (hate detection in our case). Finally, in our third approach, we evaluated the efficacy of VMLMs via few-sh","authors_text":"Aakash Kumar, Debajyoti Mazumder, Jasabanta Patro","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T10:26:54Z","title":"Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12761","kind":"arxiv","version":2},"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:a1669c33357e01b208b92b562afb1bbaef3af245c8ebf4b89b965a64778eec5a","target":"record","created_at":"2026-07-05T12:06:04Z","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":"0de43731dd0009087701b69ef32899220963b54e3255e8fd21f7c21045679bd3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T10:26:54Z","title_canon_sha256":"96e2f94acdd1fe3c2f7191f0fe3803794685bb38d9ff16b2c9044e2156af0065"},"schema_version":"1.0","source":{"id":"2412.12761","kind":"arxiv","version":2}},"canonical_sha256":"3a6d33e9c251ba70c13d87b0a41bd987dd1ac3d3c5e289545b4ca696bb74cf0a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a6d33e9c251ba70c13d87b0a41bd987dd1ac3d3c5e289545b4ca696bb74cf0a","first_computed_at":"2026-07-05T12:06:04.053703Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:06:04.053703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5Kz329MkpE7zAv0DqQASTZ9/X0E3XVRO/CPguBIXL5urLgQAaV5tiSjLcj7egbLjLEbYdhitVyaRdYEkJJlsAA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:06:04.054232Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.12761","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1669c33357e01b208b92b562afb1bbaef3af245c8ebf4b89b965a64778eec5a","sha256:1aea20db723065fcaa1d595abbb85c060889980d1352e7b5e06ada9bef44bc66"],"state_sha256":"e98dc2924d78928d4d9e576ea0d0c2a6d50e8e65990156108b2b84cf4b2a64b6"}