{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:26LZSG5EKR6QF7RWMJZTIHJS4C","short_pith_number":"pith:26LZSG5E","canonical_record":{"source":{"id":"2309.03748","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-07T14:43:17Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"5ad033f640ca1b3f7489e7c0165d98ae926b1c711488840e870abb9be3d22e1d","abstract_canon_sha256":"8abd1cd0e4eba1997a8cf9a929278e54e870e268c843ba20f2088ae2cb50351b"},"schema_version":"1.0"},"canonical_sha256":"d797991ba4547d02fe366273341d32e0a51f595440954a2b2189c90d62847540","source":{"kind":"arxiv","id":"2309.03748","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03748","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03748v1","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03748","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_12","alias_value":"26LZSG5EKR6Q","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_16","alias_value":"26LZSG5EKR6QF7RW","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_8","alias_value":"26LZSG5E","created_at":"2026-07-05T06:48:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:26LZSG5EKR6QF7RWMJZTIHJS4C","target":"record","payload":{"canonical_record":{"source":{"id":"2309.03748","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-07T14:43:17Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"5ad033f640ca1b3f7489e7c0165d98ae926b1c711488840e870abb9be3d22e1d","abstract_canon_sha256":"8abd1cd0e4eba1997a8cf9a929278e54e870e268c843ba20f2088ae2cb50351b"},"schema_version":"1.0"},"canonical_sha256":"d797991ba4547d02fe366273341d32e0a51f595440954a2b2189c90d62847540","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:41.507375Z","signature_b64":"2AQ2fXdQMemDieAEMmRzctrSQSycpmbftM76CPNLjdshXvDAIZhm4mGg0Q6ciwWUIjwaOVCiJQYSGl/eQ0+UAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d797991ba4547d02fe366273341d32e0a51f595440954a2b2189c90d62847540","last_reissued_at":"2026-07-05T06:48:41.506907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:41.506907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.03748","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:48:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ahrZ2UpuvEzCQ+sNdfAbauLd4/sGtRKSX4XJkggGvVSFa90a56nGRq484Km2tGPrPPHoP81q/zGJf67Qdk+EBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:25:18.629873Z"},"content_sha256":"4533d9dbf3a91217581a01573c8a0eecdcce9f061f5120f10e3267a44c4aad90","schema_version":"1.0","event_id":"sha256:4533d9dbf3a91217581a01573c8a0eecdcce9f061f5120f10e3267a44c4aad90"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:26LZSG5EKR6QF7RWMJZTIHJS4C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Pipeline-Based Conversational Agents with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Hendrik Purwins, Klaus-Dieter Thoben, Mina Foosherian, Purna Rathnayake, Rui Teimao, Touhidul Alam","submitted_at":"2023-09-07T14:43:17Z","abstract_excerpt":"The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.03748","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/2309.03748/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":"2026-07-05T06:48:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aBLEkn2SSBNm8YbKzvRr9JJks/rGGRxEDH2ROxoA2w9Fm0SruR4nw87WccvrKye2k6HLLN7FPEhI6NODgZ/7CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:25:18.630481Z"},"content_sha256":"b26b24e9374cc800e5b1098704c1123e6478d4682debb63cf52f9ecec2faf857","schema_version":"1.0","event_id":"sha256:b26b24e9374cc800e5b1098704c1123e6478d4682debb63cf52f9ecec2faf857"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/bundle.json","state_url":"https://pith.science/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/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":"2026-08-06T17:25:18Z","links":{"resolver":"https://pith.science/pith/26LZSG5EKR6QF7RWMJZTIHJS4C","bundle":"https://pith.science/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/bundle.json","state":"https://pith.science/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/26LZSG5EKR6QF7RWMJZTIHJS4C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:26LZSG5EKR6QF7RWMJZTIHJS4C","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":"8abd1cd0e4eba1997a8cf9a929278e54e870e268c843ba20f2088ae2cb50351b","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-07T14:43:17Z","title_canon_sha256":"5ad033f640ca1b3f7489e7c0165d98ae926b1c711488840e870abb9be3d22e1d"},"schema_version":"1.0","source":{"id":"2309.03748","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03748","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03748v1","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03748","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_12","alias_value":"26LZSG5EKR6Q","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_16","alias_value":"26LZSG5EKR6QF7RW","created_at":"2026-07-05T06:48:41Z"},{"alias_kind":"pith_short_8","alias_value":"26LZSG5E","created_at":"2026-07-05T06:48:41Z"}],"graph_snapshots":[{"event_id":"sha256:b26b24e9374cc800e5b1098704c1123e6478d4682debb63cf52f9ecec2faf857","target":"graph","created_at":"2026-07-05T06:48:41Z","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/2309.03748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in con","authors_text":"Hendrik Purwins, Klaus-Dieter Thoben, Mina Foosherian, Purna Rathnayake, Rui Teimao, Touhidul Alam","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-07T14:43:17Z","title":"Enhancing Pipeline-Based Conversational Agents with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.03748","kind":"arxiv","version":1},"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":"sha256:4533d9dbf3a91217581a01573c8a0eecdcce9f061f5120f10e3267a44c4aad90","target":"record","created_at":"2026-07-05T06:48:41Z","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":"8abd1cd0e4eba1997a8cf9a929278e54e870e268c843ba20f2088ae2cb50351b","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-07T14:43:17Z","title_canon_sha256":"5ad033f640ca1b3f7489e7c0165d98ae926b1c711488840e870abb9be3d22e1d"},"schema_version":"1.0","source":{"id":"2309.03748","kind":"arxiv","version":1}},"canonical_sha256":"d797991ba4547d02fe366273341d32e0a51f595440954a2b2189c90d62847540","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d797991ba4547d02fe366273341d32e0a51f595440954a2b2189c90d62847540","first_computed_at":"2026-07-05T06:48:41.506907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:48:41.506907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2AQ2fXdQMemDieAEMmRzctrSQSycpmbftM76CPNLjdshXvDAIZhm4mGg0Q6ciwWUIjwaOVCiJQYSGl/eQ0+UAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:48:41.507375Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.03748","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4533d9dbf3a91217581a01573c8a0eecdcce9f061f5120f10e3267a44c4aad90","sha256:b26b24e9374cc800e5b1098704c1123e6478d4682debb63cf52f9ecec2faf857"],"state_sha256":"ea4be3dc76693de2c63133a8a979a674486bb593e77ce691ad6c7bbcb45c20b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k3ETseiHp7jPceHc09A+TUHpkES8J4dL+CMMkVDH9bC6qQ9akmgYbTwkLZkZhJZiyE7PpCnka8QIddDRHh5ICQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T17:25:18.636273Z","bundle_sha256":"72662d7122ea4aac1056b1c5d557f2585e2dbcfa32e7a6bee576d55e288aa2b7"}}