{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MHJOFBXD6O4MXBIQV7N4XE7TR6","short_pith_number":"pith:MHJOFBXD","schema_version":"1.0","canonical_sha256":"61d2e286e3f3b8cb8510afdbcb93f38f8aa93a82eb783c04f3fa045513bf2657","source":{"kind":"arxiv","id":"2506.02302","version":1},"attestation_state":"computed","paper":{"title":"Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amber Shore, Ameeta Agrawal, Russell Scheinberg, So Young Lee","submitted_at":"2025-06-02T22:42:33Z","abstract_excerpt":"Large language models (LLMs) can explain grammatical rules, yet they often fail to apply those rules when judging sentence acceptability. We present \"grammar prompting\", an explain-then-process paradigm: a large LLM first produces a concise explanation of the relevant syntactic phenomenon, then that explanation is fed back as additional context to the target model -- either an LLM or a smaller language model (SLM) -- before deciding which sentence of a minimal pair is grammatical. On the English BLiMP, Chinese SLING, and Russian RuBLiMP benchmarks, this simple prompt design yields substantial "},"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":"2506.02302","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-02T22:42:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"297107d1170ebfaaada459f7f1e0a3ff0389fc65b9ff583907cc80831eecb5a9","abstract_canon_sha256":"01a28155f9fdba5ba3777a770338642f95fd8176e1d7a906cd20b9eb3405fc32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:36.014068Z","signature_b64":"uNAqRcDfR7HxLu1kx4ScT2waEzL7tcus5bbkMbCOTGegj3I+WGh6oIS/H1zx6TYbA5WsPPfAVmhKi0SQkO53AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61d2e286e3f3b8cb8510afdbcb93f38f8aa93a82eb783c04f3fa045513bf2657","last_reissued_at":"2026-07-05T11:14:36.013636Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:36.013636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amber Shore, Ameeta Agrawal, Russell Scheinberg, So Young Lee","submitted_at":"2025-06-02T22:42:33Z","abstract_excerpt":"Large language models (LLMs) can explain grammatical rules, yet they often fail to apply those rules when judging sentence acceptability. We present \"grammar prompting\", an explain-then-process paradigm: a large LLM first produces a concise explanation of the relevant syntactic phenomenon, then that explanation is fed back as additional context to the target model -- either an LLM or a smaller language model (SLM) -- before deciding which sentence of a minimal pair is grammatical. On the English BLiMP, Chinese SLING, and Russian RuBLiMP benchmarks, this simple prompt design yields substantial "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02302","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/2506.02302/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":"2506.02302","created_at":"2026-07-05T11:14:36.013693+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02302v1","created_at":"2026-07-05T11:14:36.013693+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02302","created_at":"2026-07-05T11:14:36.013693+00:00"},{"alias_kind":"pith_short_12","alias_value":"MHJOFBXD6O4M","created_at":"2026-07-05T11:14:36.013693+00:00"},{"alias_kind":"pith_short_16","alias_value":"MHJOFBXD6O4MXBIQ","created_at":"2026-07-05T11:14:36.013693+00:00"},{"alias_kind":"pith_short_8","alias_value":"MHJOFBXD","created_at":"2026-07-05T11:14:36.013693+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00425","citing_title":"The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6","json":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6.json","graph_json":"https://pith.science/api/pith-number/MHJOFBXD6O4MXBIQV7N4XE7TR6/graph.json","events_json":"https://pith.science/api/pith-number/MHJOFBXD6O4MXBIQV7N4XE7TR6/events.json","paper":"https://pith.science/paper/MHJOFBXD"},"agent_actions":{"view_html":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6","download_json":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6.json","view_paper":"https://pith.science/paper/MHJOFBXD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02302&json=true","fetch_graph":"https://pith.science/api/pith-number/MHJOFBXD6O4MXBIQV7N4XE7TR6/graph.json","fetch_events":"https://pith.science/api/pith-number/MHJOFBXD6O4MXBIQV7N4XE7TR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6/action/storage_attestation","attest_author":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6/action/author_attestation","sign_citation":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6/action/citation_signature","submit_replication":"https://pith.science/pith/MHJOFBXD6O4MXBIQV7N4XE7TR6/action/replication_record"}},"created_at":"2026-07-05T11:14:36.013693+00:00","updated_at":"2026-07-05T11:14:36.013693+00:00"}