{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MIVBNJ7OBZGHVNNIY7MPKIBABS","short_pith_number":"pith:MIVBNJ7O","schema_version":"1.0","canonical_sha256":"622a16a7ee0e4c7ab5a8c7d8f520200cb0758eb092776ae80b9f5ee1fd96c544","source":{"kind":"arxiv","id":"2410.07035","version":1},"attestation_state":"computed","paper":{"title":"PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Feiyu Duan, Jie Fu, Ke Xu, Wangchunshu Zhou, Wenhao Huang, Yibo Zhang, Zekun Wang","submitted_at":"2024-10-09T16:15:36Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, frequently failing to adhere to specific length constraints due to their token-level operations and insufficient training on data with strict length limitations. We identify this issue as stemming from a lack of positional awareness and propose novel approaches--PositionID Prompting and PositionID Fine-Tuning--to address it. These methods enhance the"},"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":"2410.07035","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-09T16:15:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b9ed641e9594ff7ddd91834b7e50a88f67bac64090a879fe5eee580b4f91024f","abstract_canon_sha256":"47d57e3850b60acfce54c224d2e14892190757138087c4be6a6e7b26b2c315e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:09.935762Z","signature_b64":"t8ySdhZyEvJC7NzGAE7hjtaEAEI5SEQXZ1VcI3dx23WvyJaMSBys53gfS2Z5PGfDJrYmefnFcrbm8L8YEWfEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"622a16a7ee0e4c7ab5a8c7d8f520200cb0758eb092776ae80b9f5ee1fd96c544","last_reissued_at":"2026-07-05T09:18:09.935199Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:09.935199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Feiyu Duan, Jie Fu, Ke Xu, Wangchunshu Zhou, Wenhao Huang, Yibo Zhang, Zekun Wang","submitted_at":"2024-10-09T16:15:36Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, frequently failing to adhere to specific length constraints due to their token-level operations and insufficient training on data with strict length limitations. We identify this issue as stemming from a lack of positional awareness and propose novel approaches--PositionID Prompting and PositionID Fine-Tuning--to address it. These methods enhance the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07035","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/2410.07035/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":"2410.07035","created_at":"2026-07-05T09:18:09.935260+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.07035v1","created_at":"2026-07-05T09:18:09.935260+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07035","created_at":"2026-07-05T09:18:09.935260+00:00"},{"alias_kind":"pith_short_12","alias_value":"MIVBNJ7OBZGH","created_at":"2026-07-05T09:18:09.935260+00:00"},{"alias_kind":"pith_short_16","alias_value":"MIVBNJ7OBZGHVNNI","created_at":"2026-07-05T09:18:09.935260+00:00"},{"alias_kind":"pith_short_8","alias_value":"MIVBNJ7O","created_at":"2026-07-05T09:18:09.935260+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS","json":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS.json","graph_json":"https://pith.science/api/pith-number/MIVBNJ7OBZGHVNNIY7MPKIBABS/graph.json","events_json":"https://pith.science/api/pith-number/MIVBNJ7OBZGHVNNIY7MPKIBABS/events.json","paper":"https://pith.science/paper/MIVBNJ7O"},"agent_actions":{"view_html":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS","download_json":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS.json","view_paper":"https://pith.science/paper/MIVBNJ7O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.07035&json=true","fetch_graph":"https://pith.science/api/pith-number/MIVBNJ7OBZGHVNNIY7MPKIBABS/graph.json","fetch_events":"https://pith.science/api/pith-number/MIVBNJ7OBZGHVNNIY7MPKIBABS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS/action/storage_attestation","attest_author":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS/action/author_attestation","sign_citation":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS/action/citation_signature","submit_replication":"https://pith.science/pith/MIVBNJ7OBZGHVNNIY7MPKIBABS/action/replication_record"}},"created_at":"2026-07-05T09:18:09.935260+00:00","updated_at":"2026-07-05T09:18:09.935260+00:00"}