{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PPDYNGHK6R4OZWI7YEXXWV5WAF","short_pith_number":"pith:PPDYNGHK","schema_version":"1.0","canonical_sha256":"7bc78698eaf478ecd91fc12f7b57b601759f41f6a5d94e5e07649927e6f44ed3","source":{"kind":"arxiv","id":"2503.13299","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Transformer Context Extension: Approaches and Evaluation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinzheng Yu, Qingfu Zhu, Yang Xu, Yijun Liu, Zhongyang Li","submitted_at":"2025-03-17T15:44:09Z","abstract_excerpt":"Large language models (LLMs) based on Transformer have been widely applied in the filed of natural language processing (NLP), demonstrating strong performance, particularly in handling short text tasks. However, when it comes to long context scenarios, the performance of LLMs degrades due to some challenges. To alleviate this phenomenon, there is a number of work proposed recently. In this survey, we first list the challenges of applying pre-trained LLMs to process long contexts. Then systematically review the approaches related to long context and propose our taxonomy categorizing them into f"},"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":"2503.13299","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:44:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"886d44c189b858b78f44faca1114feb169cd7f0025de5a3de301811a0a7a944b","abstract_canon_sha256":"5fb0b8edf1f2ffc06bdbb593226124982128792a568f6a4bd73716afcabd496d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:21.505405Z","signature_b64":"bpF/LFdDNZSA/8Y+aj0Hx8cHqmm81NllAt112GKdr7bNDw8x2CokPIc9D2AkhDdij20cbs4IxrnoNNEuCbjfBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bc78698eaf478ecd91fc12f7b57b601759f41f6a5d94e5e07649927e6f44ed3","last_reissued_at":"2026-07-05T11:33:21.504898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:21.504898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Transformer Context Extension: Approaches and Evaluation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinzheng Yu, Qingfu Zhu, Yang Xu, Yijun Liu, Zhongyang Li","submitted_at":"2025-03-17T15:44:09Z","abstract_excerpt":"Large language models (LLMs) based on Transformer have been widely applied in the filed of natural language processing (NLP), demonstrating strong performance, particularly in handling short text tasks. However, when it comes to long context scenarios, the performance of LLMs degrades due to some challenges. To alleviate this phenomenon, there is a number of work proposed recently. In this survey, we first list the challenges of applying pre-trained LLMs to process long contexts. Then systematically review the approaches related to long context and propose our taxonomy categorizing them into f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13299","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/2503.13299/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":"2503.13299","created_at":"2026-07-05T11:33:21.504963+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.13299v2","created_at":"2026-07-05T11:33:21.504963+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13299","created_at":"2026-07-05T11:33:21.504963+00:00"},{"alias_kind":"pith_short_12","alias_value":"PPDYNGHK6R4O","created_at":"2026-07-05T11:33:21.504963+00:00"},{"alias_kind":"pith_short_16","alias_value":"PPDYNGHK6R4OZWI7","created_at":"2026-07-05T11:33:21.504963+00:00"},{"alias_kind":"pith_short_8","alias_value":"PPDYNGHK","created_at":"2026-07-05T11:33:21.504963+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.03643","citing_title":"Optical Context Compression Is Just (Bad) Autoencoding","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF","json":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF.json","graph_json":"https://pith.science/api/pith-number/PPDYNGHK6R4OZWI7YEXXWV5WAF/graph.json","events_json":"https://pith.science/api/pith-number/PPDYNGHK6R4OZWI7YEXXWV5WAF/events.json","paper":"https://pith.science/paper/PPDYNGHK"},"agent_actions":{"view_html":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF","download_json":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF.json","view_paper":"https://pith.science/paper/PPDYNGHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.13299&json=true","fetch_graph":"https://pith.science/api/pith-number/PPDYNGHK6R4OZWI7YEXXWV5WAF/graph.json","fetch_events":"https://pith.science/api/pith-number/PPDYNGHK6R4OZWI7YEXXWV5WAF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF/action/storage_attestation","attest_author":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF/action/author_attestation","sign_citation":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF/action/citation_signature","submit_replication":"https://pith.science/pith/PPDYNGHK6R4OZWI7YEXXWV5WAF/action/replication_record"}},"created_at":"2026-07-05T11:33:21.504963+00:00","updated_at":"2026-07-05T11:33:21.504963+00:00"}