{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WURS6HMD5U3FFEJVUY7X7P4W2V","short_pith_number":"pith:WURS6HMD","schema_version":"1.0","canonical_sha256":"b5232f1d83ed36529135a63f7fbf96d5666268f7b653841b565e25d91d1a1946","source":{"kind":"arxiv","id":"2502.14830","version":3},"attestation_state":"computed","paper":{"title":"Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Danni Liu, Jan Niehues","submitted_at":"2025-02-20T18:45:43Z","abstract_excerpt":"While large language models demonstrate remarkable capabilities at task-specific applications through fine-tuning, extending these benefits across diverse languages is essential for broad accessibility. However, effective cross-lingual transfer is hindered by LLM performance gaps across languages and the scarcity of fine-tuning data in many languages. Through analysis of LLM internal representations from over 1,000+ language pairs, we discover that middle layers exhibit the strongest potential for cross-lingual alignment. Building on this finding, we propose a middle-layer alignment objective "},"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":"2502.14830","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:45:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"693973d723058410595870b5cf4aa3b695a396e388f6409fc10dd2bd48a5f3b8","abstract_canon_sha256":"e887303b39823f88c03b3f3a0416722e6e4ffb97675adc80f853df826b432e00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:04.341905Z","signature_b64":"epUZbhbSqc+XwDiK+Vyqur1pwKGphYdQIqjgfAqBE5JceJGdOiK5LvUgWDEqU7vHRcdxsH1HO3n/+AAF6j39Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5232f1d83ed36529135a63f7fbf96d5666268f7b653841b565e25d91d1a1946","last_reissued_at":"2026-07-05T11:14:04.341401Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:04.341401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Danni Liu, Jan Niehues","submitted_at":"2025-02-20T18:45:43Z","abstract_excerpt":"While large language models demonstrate remarkable capabilities at task-specific applications through fine-tuning, extending these benefits across diverse languages is essential for broad accessibility. However, effective cross-lingual transfer is hindered by LLM performance gaps across languages and the scarcity of fine-tuning data in many languages. Through analysis of LLM internal representations from over 1,000+ language pairs, we discover that middle layers exhibit the strongest potential for cross-lingual alignment. Building on this finding, we propose a middle-layer alignment objective "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14830","kind":"arxiv","version":3},"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/2502.14830/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":"2502.14830","created_at":"2026-07-05T11:14:04.341465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.14830v3","created_at":"2026-07-05T11:14:04.341465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14830","created_at":"2026-07-05T11:14:04.341465+00:00"},{"alias_kind":"pith_short_12","alias_value":"WURS6HMD5U3F","created_at":"2026-07-05T11:14:04.341465+00:00"},{"alias_kind":"pith_short_16","alias_value":"WURS6HMD5U3FFEJV","created_at":"2026-07-05T11:14:04.341465+00:00"},{"alias_kind":"pith_short_8","alias_value":"WURS6HMD","created_at":"2026-07-05T11:14:04.341465+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/WURS6HMD5U3FFEJVUY7X7P4W2V","json":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V.json","graph_json":"https://pith.science/api/pith-number/WURS6HMD5U3FFEJVUY7X7P4W2V/graph.json","events_json":"https://pith.science/api/pith-number/WURS6HMD5U3FFEJVUY7X7P4W2V/events.json","paper":"https://pith.science/paper/WURS6HMD"},"agent_actions":{"view_html":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V","download_json":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V.json","view_paper":"https://pith.science/paper/WURS6HMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.14830&json=true","fetch_graph":"https://pith.science/api/pith-number/WURS6HMD5U3FFEJVUY7X7P4W2V/graph.json","fetch_events":"https://pith.science/api/pith-number/WURS6HMD5U3FFEJVUY7X7P4W2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V/action/storage_attestation","attest_author":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V/action/author_attestation","sign_citation":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V/action/citation_signature","submit_replication":"https://pith.science/pith/WURS6HMD5U3FFEJVUY7X7P4W2V/action/replication_record"}},"created_at":"2026-07-05T11:14:04.341465+00:00","updated_at":"2026-07-05T11:14:04.341465+00:00"}