{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RS6ULR255MPMI6OS6NULN6LFDB","short_pith_number":"pith:RS6ULR25","schema_version":"1.0","canonical_sha256":"8cbd45c75deb1ec479d2f368b6f96518671cf0509e5fd0affd898e82775b12fd","source":{"kind":"arxiv","id":"2308.06966","version":2},"attestation_state":"computed","paper":{"title":"EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengyue Jiang, Fei Huang, Hai-Tao Zheng, Pengjun Xie, Shen Huang, Shirong Ma, Xiaobin Wang, Yangning Li, Yong Jiang","submitted_at":"2023-08-14T06:49:53Z","abstract_excerpt":"Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. However, the unique characteristics of E-commerce data pose significant challenges to general LLMs. An LLM tailored specifically for E-commerce scenarios, possessing robust cross-dataset/task generalization capabilities, is a pressing necessity. To solve this issue, in this work, we proposed the first e-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data. EcomInstruct scales up the d"},"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":"2308.06966","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-14T06:49:53Z","cross_cats_sorted":[],"title_canon_sha256":"8efb516812616e4c42a9b8e3659b037d70ad1f6590deb4cab91b270c77bb0f05","abstract_canon_sha256":"9900fa21b53dafe8ce44a25458477fbb084b14e66d15b356098a571510b2e95d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:19.555442Z","signature_b64":"1K5ek6IUYcpz6LjHLW2KKccFS0WqeGVYdPXqwTAIKxNik5ioxjR1Ym7JSVrjBM+3v87SwlDkCwUMvyI5HNYBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cbd45c75deb1ec479d2f368b6f96518671cf0509e5fd0affd898e82775b12fd","last_reissued_at":"2026-07-05T06:45:19.554969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:19.554969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengyue Jiang, Fei Huang, Hai-Tao Zheng, Pengjun Xie, Shen Huang, Shirong Ma, Xiaobin Wang, Yangning Li, Yong Jiang","submitted_at":"2023-08-14T06:49:53Z","abstract_excerpt":"Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. However, the unique characteristics of E-commerce data pose significant challenges to general LLMs. An LLM tailored specifically for E-commerce scenarios, possessing robust cross-dataset/task generalization capabilities, is a pressing necessity. To solve this issue, in this work, we proposed the first e-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data. EcomInstruct scales up the d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06966","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/2308.06966/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":"2308.06966","created_at":"2026-07-05T06:45:19.555025+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06966v2","created_at":"2026-07-05T06:45:19.555025+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06966","created_at":"2026-07-05T06:45:19.555025+00:00"},{"alias_kind":"pith_short_12","alias_value":"RS6ULR255MPM","created_at":"2026-07-05T06:45:19.555025+00:00"},{"alias_kind":"pith_short_16","alias_value":"RS6ULR255MPMI6OS","created_at":"2026-07-05T06:45:19.555025+00:00"},{"alias_kind":"pith_short_8","alias_value":"RS6ULR25","created_at":"2026-07-05T06:45:19.555025+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.08403","citing_title":"Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB","json":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB.json","graph_json":"https://pith.science/api/pith-number/RS6ULR255MPMI6OS6NULN6LFDB/graph.json","events_json":"https://pith.science/api/pith-number/RS6ULR255MPMI6OS6NULN6LFDB/events.json","paper":"https://pith.science/paper/RS6ULR25"},"agent_actions":{"view_html":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB","download_json":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB.json","view_paper":"https://pith.science/paper/RS6ULR25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06966&json=true","fetch_graph":"https://pith.science/api/pith-number/RS6ULR255MPMI6OS6NULN6LFDB/graph.json","fetch_events":"https://pith.science/api/pith-number/RS6ULR255MPMI6OS6NULN6LFDB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB/action/storage_attestation","attest_author":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB/action/author_attestation","sign_citation":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB/action/citation_signature","submit_replication":"https://pith.science/pith/RS6ULR255MPMI6OS6NULN6LFDB/action/replication_record"}},"created_at":"2026-07-05T06:45:19.555025+00:00","updated_at":"2026-07-05T06:45:19.555025+00:00"}