{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6L7SVRRZP76PS3TKUZKOPGQIB7","short_pith_number":"pith:6L7SVRRZ","schema_version":"1.0","canonical_sha256":"f2ff2ac6397ffcf96e6aa654e79a080fd6ef3dfd0e0f04cc8b623c8bd26abdb1","source":{"kind":"arxiv","id":"2305.15685","version":2},"attestation_state":"computed","paper":{"title":"RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jayakumar Hoskere, Jindong Chen, Lei Meng, Lei Shu, Liangchen Luo, Simon Tong, Yinxiao Liu, Yun Zhu","submitted_at":"2023-05-25T03:26:26Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final text results rather than intermediate revisions, it might be challenging for them to perform text rewriting tasks. Most studies in the rewriting tasks focus on a particular transformation type within the boundaries of single sentences. In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.15685","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T03:26:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c03427aab9fb019e3e1e926d3c8a64d9b40ceeadc2f3164413db852c0acaaf0d","abstract_canon_sha256":"7ea1bdcd643bf4fc6d39d14349a29cff740eb42561c4064bcea95ab93ad20087"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:14.375579Z","signature_b64":"1knPLk5paclK6abQjL9Kg+68Z9hxMeor6Zm2rblNk3UBoY7ZgE9tuBwpwCR0zpo92ZohgS/ZOBFppGvHgP0BDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2ff2ac6397ffcf96e6aa654e79a080fd6ef3dfd0e0f04cc8b623c8bd26abdb1","last_reissued_at":"2026-07-05T07:26:14.375081Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:14.375081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jayakumar Hoskere, Jindong Chen, Lei Meng, Lei Shu, Liangchen Luo, Simon Tong, Yinxiao Liu, Yun Zhu","submitted_at":"2023-05-25T03:26:26Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final text results rather than intermediate revisions, it might be challenging for them to perform text rewriting tasks. Most studies in the rewriting tasks focus on a particular transformation type within the boundaries of single sentences. In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15685","kind":"arxiv","version":2},"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/2305.15685/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.15685","created_at":"2026-07-05T07:26:14.375149+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15685v2","created_at":"2026-07-05T07:26:14.375149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15685","created_at":"2026-07-05T07:26:14.375149+00:00"},{"alias_kind":"pith_short_12","alias_value":"6L7SVRRZP76P","created_at":"2026-07-05T07:26:14.375149+00:00"},{"alias_kind":"pith_short_16","alias_value":"6L7SVRRZP76PS3TK","created_at":"2026-07-05T07:26:14.375149+00:00"},{"alias_kind":"pith_short_8","alias_value":"6L7SVRRZ","created_at":"2026-07-05T07:26:14.375149+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.03857","citing_title":"Progressive Document-level Text Simplification via Large Language Models","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7","json":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7.json","graph_json":"https://pith.science/api/pith-number/6L7SVRRZP76PS3TKUZKOPGQIB7/graph.json","events_json":"https://pith.science/api/pith-number/6L7SVRRZP76PS3TKUZKOPGQIB7/events.json","paper":"https://pith.science/paper/6L7SVRRZ"},"agent_actions":{"view_html":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7","download_json":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7.json","view_paper":"https://pith.science/paper/6L7SVRRZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15685&json=true","fetch_graph":"https://pith.science/api/pith-number/6L7SVRRZP76PS3TKUZKOPGQIB7/graph.json","fetch_events":"https://pith.science/api/pith-number/6L7SVRRZP76PS3TKUZKOPGQIB7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7/action/storage_attestation","attest_author":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7/action/author_attestation","sign_citation":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7/action/citation_signature","submit_replication":"https://pith.science/pith/6L7SVRRZP76PS3TKUZKOPGQIB7/action/replication_record"}},"created_at":"2026-07-05T07:26:14.375149+00:00","updated_at":"2026-07-05T07:26:14.375149+00:00"}