{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:6KSY5GLIPJHASFWUPAPW3RUXEW","short_pith_number":"pith:6KSY5GLI","canonical_record":{"source":{"id":"2307.04172","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-09T13:38:25Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"a8e6edcfa7f2076954b9ac77680a66db5964f20b303e0ddebf46da890bcf918c","abstract_canon_sha256":"5f921f2e735babd3cc198fa3a14cfe57cd15211b2ecf2e18e3e87d511e1c05a6"},"schema_version":"1.0"},"canonical_sha256":"f2a58e99687a4e0916d4781f6dc69725af31de25125b240c7483a39716ab2ecd","source":{"kind":"arxiv","id":"2307.04172","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.04172","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"arxiv_version","alias_value":"2307.04172v2","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.04172","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_12","alias_value":"6KSY5GLIPJHA","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_16","alias_value":"6KSY5GLIPJHASFWU","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_8","alias_value":"6KSY5GLI","created_at":"2026-07-05T06:55:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:6KSY5GLIPJHASFWUPAPW3RUXEW","target":"record","payload":{"canonical_record":{"source":{"id":"2307.04172","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-09T13:38:25Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"a8e6edcfa7f2076954b9ac77680a66db5964f20b303e0ddebf46da890bcf918c","abstract_canon_sha256":"5f921f2e735babd3cc198fa3a14cfe57cd15211b2ecf2e18e3e87d511e1c05a6"},"schema_version":"1.0"},"canonical_sha256":"f2a58e99687a4e0916d4781f6dc69725af31de25125b240c7483a39716ab2ecd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:55:24.260658Z","signature_b64":"YhnsJjNW+tTdddN6RpVMaHYxc195zVFijpSjAGfsStTBk3Pvu9lwlLbVxPyyB3eTl3Gp1xNqu1mlofswQXfNBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2a58e99687a4e0916d4781f6dc69725af31de25125b240c7483a39716ab2ecd","last_reissued_at":"2026-07-05T06:55:24.260168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:55:24.260168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.04172","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:55:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GFNp2m/GfVcCsWLmv1mniW0kZ3h/ZqnZamafbtHk35s6Z3WT2jnDDvUcu4/siSozcpQOsR+wEJ5GNW30sPFGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:19:06.168274Z"},"content_sha256":"fdb055fdab7846d90f77634a4f70abf1375cd3f7278017f9de3d841f85bd87e2","schema_version":"1.0","event_id":"sha256:fdb055fdab7846d90f77634a4f70abf1375cd3f7278017f9de3d841f85bd87e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:6KSY5GLIPJHASFWUPAPW3RUXEW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Generative Large Language Models Perform ASR Error Correction?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Kate Knill, Mark Gales, Mengjie Qian, Potsawee Manakul, Rao Ma","submitted_at":"2023-07-09T13:38:25Z","abstract_excerpt":"ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using the decoding results of a target ASR system. This approach can be computationally intensive and the model is tuned to a specific ASR system. Recently generative large language models (LLMs) have been applied to a wide range of natural language processing tasks, as they can operate in a zero-shot or few shot fashion. In this paper we investigate using ChatGPT, a generative LLM, for ASR error correction. Based on the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.04172","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/2307.04172/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:55:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yf1RDbxxrbmbitEXW/uvk2FfX8YkszMG9spf1sVyrw+pVW1Aps6BpJ5+n42yA1MBruAH48CvqsU1Nn5bHFSACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:19:06.168856Z"},"content_sha256":"72c387470cdd6fe8e220deff36c3453e7873624430ebd614950c1e134659a0a7","schema_version":"1.0","event_id":"sha256:72c387470cdd6fe8e220deff36c3453e7873624430ebd614950c1e134659a0a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/bundle.json","state_url":"https://pith.science/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T20:19:06Z","links":{"resolver":"https://pith.science/pith/6KSY5GLIPJHASFWUPAPW3RUXEW","bundle":"https://pith.science/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/bundle.json","state":"https://pith.science/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6KSY5GLIPJHASFWUPAPW3RUXEW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:6KSY5GLIPJHASFWUPAPW3RUXEW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5f921f2e735babd3cc198fa3a14cfe57cd15211b2ecf2e18e3e87d511e1c05a6","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-09T13:38:25Z","title_canon_sha256":"a8e6edcfa7f2076954b9ac77680a66db5964f20b303e0ddebf46da890bcf918c"},"schema_version":"1.0","source":{"id":"2307.04172","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.04172","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"arxiv_version","alias_value":"2307.04172v2","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.04172","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_12","alias_value":"6KSY5GLIPJHA","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_16","alias_value":"6KSY5GLIPJHASFWU","created_at":"2026-07-05T06:55:24Z"},{"alias_kind":"pith_short_8","alias_value":"6KSY5GLI","created_at":"2026-07-05T06:55:24Z"}],"graph_snapshots":[{"event_id":"sha256:72c387470cdd6fe8e220deff36c3453e7873624430ebd614950c1e134659a0a7","target":"graph","created_at":"2026-07-05T06:55:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.04172/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using the decoding results of a target ASR system. This approach can be computationally intensive and the model is tuned to a specific ASR system. Recently generative large language models (LLMs) have been applied to a wide range of natural language processing tasks, as they can operate in a zero-shot or few shot fashion. In this paper we investigate using ChatGPT, a generative LLM, for ASR error correction. Based on the ","authors_text":"Kate Knill, Mark Gales, Mengjie Qian, Potsawee Manakul, Rao Ma","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-09T13:38:25Z","title":"Can Generative Large Language Models Perform ASR Error Correction?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.04172","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fdb055fdab7846d90f77634a4f70abf1375cd3f7278017f9de3d841f85bd87e2","target":"record","created_at":"2026-07-05T06:55:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5f921f2e735babd3cc198fa3a14cfe57cd15211b2ecf2e18e3e87d511e1c05a6","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-09T13:38:25Z","title_canon_sha256":"a8e6edcfa7f2076954b9ac77680a66db5964f20b303e0ddebf46da890bcf918c"},"schema_version":"1.0","source":{"id":"2307.04172","kind":"arxiv","version":2}},"canonical_sha256":"f2a58e99687a4e0916d4781f6dc69725af31de25125b240c7483a39716ab2ecd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2a58e99687a4e0916d4781f6dc69725af31de25125b240c7483a39716ab2ecd","first_computed_at":"2026-07-05T06:55:24.260168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:55:24.260168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YhnsJjNW+tTdddN6RpVMaHYxc195zVFijpSjAGfsStTBk3Pvu9lwlLbVxPyyB3eTl3Gp1xNqu1mlofswQXfNBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:55:24.260658Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.04172","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fdb055fdab7846d90f77634a4f70abf1375cd3f7278017f9de3d841f85bd87e2","sha256:72c387470cdd6fe8e220deff36c3453e7873624430ebd614950c1e134659a0a7"],"state_sha256":"18f8f168a981ef016be936ec515a8d9f04ea1ba20d57cb0ea49a3faf774441ba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xdre1DdiJJzZrfMotpMIWv1iSXJLAxjWZcLUywOGbxVjyTqLXM9fF2BfOwe7547JyJ+0YwhvLKbQ3lcTcoMxDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:19:06.174310Z","bundle_sha256":"1f7abbc7e94d0f0e6375e465f0ebcaaa2d9b894c5e3b31a3c48e80cbc6258ef7"}}