{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R5OXNQT2JSNVMARCAU7VIFRWMY","short_pith_number":"pith:R5OXNQT2","schema_version":"1.0","canonical_sha256":"8f5d76c27a4c9b560222053f5416366620e017c31d45613d1cd159de8d57dc80","source":{"kind":"arxiv","id":"2403.15157","version":2},"attestation_state":"computed","paper":{"title":"AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chaoyun Zhang, Dongmei Zhang, Minghua Ma, Qingwei Lin, Qi Zhang, Saravan Rajmohan, Shilin He, Si Qin, Xiaoting Qin, Xiaoyu Gou, Yajie Xue, Yuhao Wu, Yu Kang, Yuyi Liang, Zicheng Ma","submitted_at":"2024-03-22T12:13:16Z","abstract_excerpt":"Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural language interface, leveraging large language models (LLMs). Allhands adheres to a conventional feedback analytic workflow, initially conducting classification and topic modeling on the feedback to convert them into a structurally augmente"},"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":"2403.15157","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-03-22T12:13:16Z","cross_cats_sorted":[],"title_canon_sha256":"a39d83119a6689dc394c94b2e442d93bbb259c9a831937b6b88ff4d0f7c98021","abstract_canon_sha256":"533151b16377f84a3d877b7dd15105c6df975151c56284c5dfce56771bb727ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:50.548618Z","signature_b64":"b9Xkz2XaztfAbWv1Rc4BURZdffwFkfXB9emap/fmQmq+XvXlJfW01hyJNd5+FHHCpoyzMgwE2hdau4XChex1AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f5d76c27a4c9b560222053f5416366620e017c31d45613d1cd159de8d57dc80","last_reissued_at":"2026-07-05T08:03:50.548150Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:50.548150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chaoyun Zhang, Dongmei Zhang, Minghua Ma, Qingwei Lin, Qi Zhang, Saravan Rajmohan, Shilin He, Si Qin, Xiaoting Qin, Xiaoyu Gou, Yajie Xue, Yuhao Wu, Yu Kang, Yuyi Liang, Zicheng Ma","submitted_at":"2024-03-22T12:13:16Z","abstract_excerpt":"Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural language interface, leveraging large language models (LLMs). Allhands adheres to a conventional feedback analytic workflow, initially conducting classification and topic modeling on the feedback to convert them into a structurally augmente"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15157","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/2403.15157/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":"2403.15157","created_at":"2026-07-05T08:03:50.548207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.15157v2","created_at":"2026-07-05T08:03:50.548207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15157","created_at":"2026-07-05T08:03:50.548207+00:00"},{"alias_kind":"pith_short_12","alias_value":"R5OXNQT2JSNV","created_at":"2026-07-05T08:03:50.548207+00:00"},{"alias_kind":"pith_short_16","alias_value":"R5OXNQT2JSNVMARC","created_at":"2026-07-05T08:03:50.548207+00:00"},{"alias_kind":"pith_short_8","alias_value":"R5OXNQT2","created_at":"2026-07-05T08:03:50.548207+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.13766","citing_title":"A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards","ref_index":192,"is_internal_anchor":false},{"citing_arxiv_id":"2411.18279","citing_title":"Large Language Model-Brained GUI Agents: A Survey","ref_index":96,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY","json":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY.json","graph_json":"https://pith.science/api/pith-number/R5OXNQT2JSNVMARCAU7VIFRWMY/graph.json","events_json":"https://pith.science/api/pith-number/R5OXNQT2JSNVMARCAU7VIFRWMY/events.json","paper":"https://pith.science/paper/R5OXNQT2"},"agent_actions":{"view_html":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY","download_json":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY.json","view_paper":"https://pith.science/paper/R5OXNQT2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.15157&json=true","fetch_graph":"https://pith.science/api/pith-number/R5OXNQT2JSNVMARCAU7VIFRWMY/graph.json","fetch_events":"https://pith.science/api/pith-number/R5OXNQT2JSNVMARCAU7VIFRWMY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY/action/storage_attestation","attest_author":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY/action/author_attestation","sign_citation":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY/action/citation_signature","submit_replication":"https://pith.science/pith/R5OXNQT2JSNVMARCAU7VIFRWMY/action/replication_record"}},"created_at":"2026-07-05T08:03:50.548207+00:00","updated_at":"2026-07-05T08:03:50.548207+00:00"}