{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2CSB7O6IWES22YSJUWKXDCPNJB","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":"59d034c78160c82208f495a329f2f4e154783f7dd697c756e283b817d6eabdf2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-07T16:34:28Z","title_canon_sha256":"e8e211bb97abb76101deceae85f109d6b2239878e707477a9b6a5d2fb3352731"},"schema_version":"1.0","source":{"id":"2507.05179","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05179","created_at":"2026-06-02T01:04:14Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05179v6","created_at":"2026-06-02T01:04:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05179","created_at":"2026-06-02T01:04:14Z"},{"alias_kind":"pith_short_12","alias_value":"2CSB7O6IWES2","created_at":"2026-06-02T01:04:14Z"},{"alias_kind":"pith_short_16","alias_value":"2CSB7O6IWES22YSJ","created_at":"2026-06-02T01:04:14Z"},{"alias_kind":"pith_short_8","alias_value":"2CSB7O6I","created_at":"2026-06-02T01:04:14Z"}],"graph_snapshots":[{"event_id":"sha256:55ff934d1e1baefc0140b4e6816c37b2166316070258492b5e4f6cff9ba3bbdb","target":"graph","created_at":"2026-06-02T01:04:14Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experiments with LLMs (Mistral, Llama, Gemma) and PLMs (mBART, mT5) confirm the framework's effectiveness in generating coherent, contextually relevant explanations."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That fact-checked explanations from credible sources can reliably serve as preferred responses while LLM outputs serve as non-preferred responses, and that adding the Actuality and Finesse parameters to the DPO loss will produce measurable improvements in explanation quality without introducing new biases or inconsistencies."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A DPO framework augmented with curriculum learning and two new loss parameters generates veracity explanations for Hindi news using LLMs and PLMs."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A curriculum-driven DPO framework generates reliable Hindi news veracity explanations by preferring fact-checked sources."}],"snapshot_sha256":"9223b97f260b554eca0cbb36ee18200db00605ddb6e852a8b26544a2b50d6c62"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"48a212deabcf3159a1b8e2f3a7419ec5b21e20f66ccd7d1731cbee01a61a6932"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.05179/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In an era of rampant misinformation, generating reliable news explanations is vital, especially for under-represented languages like Hindi. Lacking robust automated tools, Hindi faces challenges in scaling misinformation detection. To bridge this gap, we propose a novel framework integrating Direct Preference Optimization (DPO) with curriculum learning to align machine-generated explanations with human reasoning. Fact-checked explanations from credible sources serve as preferred responses, while LLM outputs highlight system limitations and serve as non-preferred responses. To refine task-speci","authors_text":"Adam Jatowt, Pulkit Bansal, Raghvendra Kumar, Shakti Singh, Sriparna Saha","cross_cats":[],"headline":"A curriculum-driven DPO framework generates reliable Hindi news veracity explanations by preferring fact-checked sources.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-07T16:34:28Z","title":"From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05179","kind":"arxiv","version":6},"verdict":{"created_at":"2026-05-19T06:04:03.724054Z","id":"2ce27f78-c6e8-44ec-8fc4-003038fbf60c","model_set":{"reader":"grok-4.3"},"one_line_summary":"A DPO framework augmented with curriculum learning and two new loss parameters generates veracity explanations for Hindi news using LLMs and PLMs.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A curriculum-driven DPO framework generates reliable Hindi news veracity explanations by preferring fact-checked sources.","strongest_claim":"Experiments with LLMs (Mistral, Llama, Gemma) and PLMs (mBART, mT5) confirm the framework's effectiveness in generating coherent, contextually relevant explanations.","weakest_assumption":"That fact-checked explanations from credible sources can reliably serve as preferred responses while LLM outputs serve as non-preferred responses, and that adding the Actuality and Finesse parameters to the DPO loss will produce measurable improvements in explanation quality without introducing new biases or inconsistencies."}},"verdict_id":"2ce27f78-c6e8-44ec-8fc4-003038fbf60c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e28391e4fac4f5dbb4361b478da48c0d3e8122ebe1f71c3cb9e870440b9632c3","target":"record","created_at":"2026-06-02T01:04:14Z","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":"59d034c78160c82208f495a329f2f4e154783f7dd697c756e283b817d6eabdf2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-07T16:34:28Z","title_canon_sha256":"e8e211bb97abb76101deceae85f109d6b2239878e707477a9b6a5d2fb3352731"},"schema_version":"1.0","source":{"id":"2507.05179","kind":"arxiv","version":6}},"canonical_sha256":"d0a41fbbc8b125ad6249a5957189ed4860ee135f4fafd789158d3e51fa8e0cfa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0a41fbbc8b125ad6249a5957189ed4860ee135f4fafd789158d3e51fa8e0cfa","first_computed_at":"2026-06-02T01:04:14.825707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T01:04:14.825707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TzXtAzPDKjJeLhm7kN40/fWw2FELuj676+8RjmpZjBcfUfSCD+YEiTbbPTf+uSq4Z76esl9zTJGr+G+77aUJAg==","signature_status":"signed_v1","signed_at":"2026-06-02T01:04:14.826187Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.05179","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e28391e4fac4f5dbb4361b478da48c0d3e8122ebe1f71c3cb9e870440b9632c3","sha256:55ff934d1e1baefc0140b4e6816c37b2166316070258492b5e4f6cff9ba3bbdb"],"state_sha256":"05714554d2133afbabd86fb9cdcf1a0c125a38daa78fcbae765dc4929954b459"}