{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5TLYPTLLSNWCZSASNTRUQEXNYV","short_pith_number":"pith:5TLYPTLL","schema_version":"1.0","canonical_sha256":"ecd787cd6b936c2cc8126ce34812edc56d6c4637b7679df8914cba941a6c3a06","source":{"kind":"arxiv","id":"2312.16233","version":1},"attestation_state":"computed","paper":{"title":"Chatbot is Not All You Need: Information-rich Prompting for More Realistic Responses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Assentay Makhmud, Seokhoon Jeong","submitted_at":"2023-12-25T02:18:58Z","abstract_excerpt":"Recent Large Language Models (LLMs) have shown remarkable capabilities in mimicking fictional characters or real humans in conversational settings. However, the realism and consistency of these responses can be further enhanced by providing richer information of the agent being mimicked. In this paper, we propose a novel approach to generate more realistic and consistent responses from LLMs, leveraging five senses, attributes, emotional states, relationship with the interlocutor, and memories. By incorporating these factors, we aim to increase the LLM's capacity for generating natural and real"},"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":"2312.16233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-25T02:18:58Z","cross_cats_sorted":[],"title_canon_sha256":"b994883cbb287915b17874d778168d1bbe09c51141c52ec267877a0309c9a93a","abstract_canon_sha256":"765cea00a1bb01e9e83c6aa843ef8e24e04a14212a663cd646bc146792907b46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:14.784797Z","signature_b64":"ZBCupD0KVo1MLU6f8pLtT+3f5pXSKTh2xapIedWyIAx7dHviZ5EFAesVPmROrZrrQN/tLdd3hnkNva4/ySErBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecd787cd6b936c2cc8126ce34812edc56d6c4637b7679df8914cba941a6c3a06","last_reissued_at":"2026-07-05T07:28:14.784363Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:14.784363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chatbot is Not All You Need: Information-rich Prompting for More Realistic Responses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Assentay Makhmud, Seokhoon Jeong","submitted_at":"2023-12-25T02:18:58Z","abstract_excerpt":"Recent Large Language Models (LLMs) have shown remarkable capabilities in mimicking fictional characters or real humans in conversational settings. However, the realism and consistency of these responses can be further enhanced by providing richer information of the agent being mimicked. In this paper, we propose a novel approach to generate more realistic and consistent responses from LLMs, leveraging five senses, attributes, emotional states, relationship with the interlocutor, and memories. By incorporating these factors, we aim to increase the LLM's capacity for generating natural and real"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.16233","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/2312.16233/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":"2312.16233","created_at":"2026-07-05T07:28:14.784422+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.16233v1","created_at":"2026-07-05T07:28:14.784422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16233","created_at":"2026-07-05T07:28:14.784422+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TLYPTLLSNWC","created_at":"2026-07-05T07:28:14.784422+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TLYPTLLSNWCZSAS","created_at":"2026-07-05T07:28:14.784422+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TLYPTLL","created_at":"2026-07-05T07:28:14.784422+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV","json":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV.json","graph_json":"https://pith.science/api/pith-number/5TLYPTLLSNWCZSASNTRUQEXNYV/graph.json","events_json":"https://pith.science/api/pith-number/5TLYPTLLSNWCZSASNTRUQEXNYV/events.json","paper":"https://pith.science/paper/5TLYPTLL"},"agent_actions":{"view_html":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV","download_json":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV.json","view_paper":"https://pith.science/paper/5TLYPTLL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.16233&json=true","fetch_graph":"https://pith.science/api/pith-number/5TLYPTLLSNWCZSASNTRUQEXNYV/graph.json","fetch_events":"https://pith.science/api/pith-number/5TLYPTLLSNWCZSASNTRUQEXNYV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV/action/storage_attestation","attest_author":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV/action/author_attestation","sign_citation":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV/action/citation_signature","submit_replication":"https://pith.science/pith/5TLYPTLLSNWCZSASNTRUQEXNYV/action/replication_record"}},"created_at":"2026-07-05T07:28:14.784422+00:00","updated_at":"2026-07-05T07:28:14.784422+00:00"}