{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NV3TWYHGCA3CC5LRE4FOY4H4KD","short_pith_number":"pith:NV3TWYHG","schema_version":"1.0","canonical_sha256":"6d773b60e61036217571270aec70fc50c071a9f3ddece467922313cabdfc3a23","source":{"kind":"arxiv","id":"2310.01783","version":1},"attestation_state":"computed","paper":{"title":"Can large language models provide useful feedback on research papers? A large-scale empirical analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.HC"],"primary_cat":"cs.LG","authors_text":"Binglu Wang, Daisy Ding, Daniel McFarland, Daniel Smith, Hancheng Cao, James Zou, Kailas Vodrahalli, Siyu He, Weixin Liang, Xinyu Yang, Yian Yin, Yuhui Zhang","submitted_at":"2023-10-03T04:14:17Z","abstract_excerpt":"Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are increasingly difficult to obtain. Researchers who are more junior or from under-resourced settings have especially hard times getting timely feedback. With the breakthrough of large language models (LLM) such as GPT-4, there is growing interest in using LLMs to generate scientific feedback on research manuscripts. However, the utility of LLM-generated feedback h"},"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":"2310.01783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T04:14:17Z","cross_cats_sorted":["cs.AI","cs.CL","cs.HC"],"title_canon_sha256":"5b38b50551890b092c5313544e7b8185862b715542fecdabc18aa089924d497d","abstract_canon_sha256":"c89b79006e7a277afffd5f7c60925c9ffe38697421f2e7552f2115dfdbf0524e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:45.944056Z","signature_b64":"+cvyXGsruubifDExQtRjKdx6eqZzvW0GwGAOSXqkgQjyaqPaVlGN5JD7Wm2KWlX1xDLoVazWexKk9hpkg2t7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d773b60e61036217571270aec70fc50c071a9f3ddece467922313cabdfc3a23","last_reissued_at":"2026-07-05T06:56:45.943561Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:45.943561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can large language models provide useful feedback on research papers? A large-scale empirical analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.HC"],"primary_cat":"cs.LG","authors_text":"Binglu Wang, Daisy Ding, Daniel McFarland, Daniel Smith, Hancheng Cao, James Zou, Kailas Vodrahalli, Siyu He, Weixin Liang, Xinyu Yang, Yian Yin, Yuhui Zhang","submitted_at":"2023-10-03T04:14:17Z","abstract_excerpt":"Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are increasingly difficult to obtain. Researchers who are more junior or from under-resourced settings have especially hard times getting timely feedback. With the breakthrough of large language models (LLM) such as GPT-4, there is growing interest in using LLMs to generate scientific feedback on research manuscripts. However, the utility of LLM-generated feedback h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01783","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/2310.01783/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":"2310.01783","created_at":"2026-07-05T06:56:45.943628+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.01783v1","created_at":"2026-07-05T06:56:45.943628+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01783","created_at":"2026-07-05T06:56:45.943628+00:00"},{"alias_kind":"pith_short_12","alias_value":"NV3TWYHGCA3C","created_at":"2026-07-05T06:56:45.943628+00:00"},{"alias_kind":"pith_short_16","alias_value":"NV3TWYHGCA3CC5LR","created_at":"2026-07-05T06:56:45.943628+00:00"},{"alias_kind":"pith_short_8","alias_value":"NV3TWYHG","created_at":"2026-07-05T06:56:45.943628+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24530","citing_title":"NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31651","citing_title":"FARS: A Fully Automated Research System Deployed at Scale","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25172","citing_title":"Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13349","citing_title":"From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2406.12708","citing_title":"AgentReview: Exploring Peer Review Dynamics with LLM Agents","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07855","citing_title":"Jagged AI in Scientific Peer Review: Evidence from POMP Data Analysis","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04074","citing_title":"FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07855","citing_title":"Jagged AI in Scientific Peer Review: Evidence from POMP Data Analysis","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD","json":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD.json","graph_json":"https://pith.science/api/pith-number/NV3TWYHGCA3CC5LRE4FOY4H4KD/graph.json","events_json":"https://pith.science/api/pith-number/NV3TWYHGCA3CC5LRE4FOY4H4KD/events.json","paper":"https://pith.science/paper/NV3TWYHG"},"agent_actions":{"view_html":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD","download_json":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD.json","view_paper":"https://pith.science/paper/NV3TWYHG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.01783&json=true","fetch_graph":"https://pith.science/api/pith-number/NV3TWYHGCA3CC5LRE4FOY4H4KD/graph.json","fetch_events":"https://pith.science/api/pith-number/NV3TWYHGCA3CC5LRE4FOY4H4KD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD/action/storage_attestation","attest_author":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD/action/author_attestation","sign_citation":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD/action/citation_signature","submit_replication":"https://pith.science/pith/NV3TWYHGCA3CC5LRE4FOY4H4KD/action/replication_record"}},"created_at":"2026-07-05T06:56:45.943628+00:00","updated_at":"2026-07-05T06:56:45.943628+00:00"}