{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:CAFF6KBSP7LQRS3MGK4G2DYI5W","short_pith_number":"pith:CAFF6KBS","canonical_record":{"source":{"id":"2305.12676","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T03:28:48Z","cross_cats_sorted":[],"title_canon_sha256":"54b5c5fa0690dcf5e915e2621498843734a2f322180a9afd3bf1d1655c6abe96","abstract_canon_sha256":"b3b1c177e822621025efe0ea05ceba2c1bb9008451ee8dda46b5a1e4bade0bd7"},"schema_version":"1.0"},"canonical_sha256":"100a5f28327fd708cb6c32b86d0f08eda11feaec471d47e8973d063848599ea5","source":{"kind":"arxiv","id":"2305.12676","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12676","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12676v3","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12676","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"CAFF6KBSP7LQ","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_16","alias_value":"CAFF6KBSP7LQRS3M","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_8","alias_value":"CAFF6KBS","created_at":"2026-07-05T06:14:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:CAFF6KBSP7LQRS3MGK4G2DYI5W","target":"record","payload":{"canonical_record":{"source":{"id":"2305.12676","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T03:28:48Z","cross_cats_sorted":[],"title_canon_sha256":"54b5c5fa0690dcf5e915e2621498843734a2f322180a9afd3bf1d1655c6abe96","abstract_canon_sha256":"b3b1c177e822621025efe0ea05ceba2c1bb9008451ee8dda46b5a1e4bade0bd7"},"schema_version":"1.0"},"canonical_sha256":"100a5f28327fd708cb6c32b86d0f08eda11feaec471d47e8973d063848599ea5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:43.003098Z","signature_b64":"TuzwxFqaguuT3bWprzt0dG4oSJFrlw2ikHzsCNWZaacKd3ntiELrAofnYlM6iQaLS3i3Ywd2ieR1sc9aPOXrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"100a5f28327fd708cb6c32b86d0f08eda11feaec471d47e8973d063848599ea5","last_reissued_at":"2026-07-05T06:14:43.002686Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:43.002686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.12676","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LnRbpa3XMeTUIjbRSo/e1bwBbn5nkiZZZcAQvNYx880RQPL2pe7B2P3SYpk9G/L5jAO1Qr6LMjjwuQxe275NCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:42:15.757008Z"},"content_sha256":"864208db94e264c649802643f45f5e38045cf9dc0bec14fe4fbacda0ee9e59d6","schema_version":"1.0","event_id":"sha256:864208db94e264c649802643f45f5e38045cf9dc0bec14fe4fbacda0ee9e59d6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:CAFF6KBSP7LQRS3MGK4G2DYI5W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hong Liu, Qing Xiao, Wenbo Zhao, Zhaobiao Lv, Zhijian Ou","submitted_at":"2023-05-22T03:28:48Z","abstract_excerpt":"Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs). As an important application, ELMs have been successfully used as a means for calculating sentence scores in speech recognition, but they all use less-modern CNN or LSTM networks. The recent progress in Transformer networks and large pretrained models such as BERT and GPT2 opens new possibility to further advancing ELMs. In this paper, we explore different architectures of energy functions and different training metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12676","kind":"arxiv","version":3},"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/2305.12676/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":"2026-07-05T06:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BZu3L/yJ2MahWjY3bkvwWB/WHODpgYYcM7H1zFa/4RYp0qfSlLH8d7rvo9RHSAtbXYaR8l5azaQik8VsXluDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:42:15.757495Z"},"content_sha256":"ac40afaf077bf580f72802360c0636af0a19b70408ed2285a62690a6e408a8f6","schema_version":"1.0","event_id":"sha256:ac40afaf077bf580f72802360c0636af0a19b70408ed2285a62690a6e408a8f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/bundle.json","state_url":"https://pith.science/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/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":"2026-08-08T15:42:15Z","links":{"resolver":"https://pith.science/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W","bundle":"https://pith.science/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/bundle.json","state":"https://pith.science/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CAFF6KBSP7LQRS3MGK4G2DYI5W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CAFF6KBSP7LQRS3MGK4G2DYI5W","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":"b3b1c177e822621025efe0ea05ceba2c1bb9008451ee8dda46b5a1e4bade0bd7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T03:28:48Z","title_canon_sha256":"54b5c5fa0690dcf5e915e2621498843734a2f322180a9afd3bf1d1655c6abe96"},"schema_version":"1.0","source":{"id":"2305.12676","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12676","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12676v3","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12676","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"CAFF6KBSP7LQ","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_16","alias_value":"CAFF6KBSP7LQRS3M","created_at":"2026-07-05T06:14:43Z"},{"alias_kind":"pith_short_8","alias_value":"CAFF6KBS","created_at":"2026-07-05T06:14:43Z"}],"graph_snapshots":[{"event_id":"sha256:ac40afaf077bf580f72802360c0636af0a19b70408ed2285a62690a6e408a8f6","target":"graph","created_at":"2026-07-05T06:14:43Z","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/2305.12676/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs). As an important application, ELMs have been successfully used as a means for calculating sentence scores in speech recognition, but they all use less-modern CNN or LSTM networks. The recent progress in Transformer networks and large pretrained models such as BERT and GPT2 opens new possibility to further advancing ELMs. In this paper, we explore different architectures of energy functions and different training metho","authors_text":"Hong Liu, Qing Xiao, Wenbo Zhao, Zhaobiao Lv, Zhijian Ou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T03:28:48Z","title":"Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12676","kind":"arxiv","version":3},"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":"sha256:864208db94e264c649802643f45f5e38045cf9dc0bec14fe4fbacda0ee9e59d6","target":"record","created_at":"2026-07-05T06:14:43Z","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":"b3b1c177e822621025efe0ea05ceba2c1bb9008451ee8dda46b5a1e4bade0bd7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T03:28:48Z","title_canon_sha256":"54b5c5fa0690dcf5e915e2621498843734a2f322180a9afd3bf1d1655c6abe96"},"schema_version":"1.0","source":{"id":"2305.12676","kind":"arxiv","version":3}},"canonical_sha256":"100a5f28327fd708cb6c32b86d0f08eda11feaec471d47e8973d063848599ea5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"100a5f28327fd708cb6c32b86d0f08eda11feaec471d47e8973d063848599ea5","first_computed_at":"2026-07-05T06:14:43.002686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:14:43.002686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TuzwxFqaguuT3bWprzt0dG4oSJFrlw2ikHzsCNWZaacKd3ntiELrAofnYlM6iQaLS3i3Ywd2ieR1sc9aPOXrDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:14:43.003098Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.12676","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:864208db94e264c649802643f45f5e38045cf9dc0bec14fe4fbacda0ee9e59d6","sha256:ac40afaf077bf580f72802360c0636af0a19b70408ed2285a62690a6e408a8f6"],"state_sha256":"1bee19e6f0526fb122b74b22923dee5490f914f987b98ac9e4792ed999232088"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8MPHL2lEDfx/PO9hduV8tSSy/Nm45QQZ35bd2NCNykdh4isaXquZ3ETX9v+S7hwd7r8MXfyIkczkGgBY7hYPDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T15:42:15.762674Z","bundle_sha256":"b0d9d05e33f00fbd5583623cf5c5a740286e1a76257c22c1af8b60afc7e2d0e2"}}