{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EJLTGXHDQJZU3SXXXAJUUMUSAB","short_pith_number":"pith:EJLTGXHD","schema_version":"1.0","canonical_sha256":"2257335ce382734dcaf7b8134a329200458c4c4052b56d2a07546c79ece5c033","source":{"kind":"arxiv","id":"2406.02018","version":2},"attestation_state":"computed","paper":{"title":"Why Would You Suggest That? Human Trust in Language Model Responses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Ho Chit Siu, Jaime D. Pe\\~na, Manasi Sharma, Rohan Paleja","submitted_at":"2024-06-04T06:57:47Z","abstract_excerpt":"The emergence of Large Language Models (LLMs) has revealed a growing need for human-AI collaboration, especially in creative decision-making scenarios where trust and reliance are paramount. Through human studies and model evaluations on the open-ended News Headline Generation task from the LaMP benchmark, we analyze how the framing and presence of explanations affect user trust and model performance. Overall, we provide evidence that adding an explanation in the model response to justify its reasoning significantly increases self-reported user trust in the model when the user has the opportun"},"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":"2406.02018","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-04T06:57:47Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"eccea0046da3b20455adb1bae101e3763b284a9cd9acefdf4cf194dd6ec8ed56","abstract_canon_sha256":"962faeea2e1b933731360b3f66ab00deaf0ccd687beddb497296e9d38dbac7a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:58.106244Z","signature_b64":"XFap4tPt+7CD1qd2NsvfIiLQ32YkKX9atqitoPG3g5scksMRKFEEMLvNXA4N7KJKzt5llYtThlbo0i3ahV8RAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2257335ce382734dcaf7b8134a329200458c4c4052b56d2a07546c79ece5c033","last_reissued_at":"2026-07-05T09:15:58.105560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:58.105560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Why Would You Suggest That? Human Trust in Language Model Responses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Ho Chit Siu, Jaime D. Pe\\~na, Manasi Sharma, Rohan Paleja","submitted_at":"2024-06-04T06:57:47Z","abstract_excerpt":"The emergence of Large Language Models (LLMs) has revealed a growing need for human-AI collaboration, especially in creative decision-making scenarios where trust and reliance are paramount. Through human studies and model evaluations on the open-ended News Headline Generation task from the LaMP benchmark, we analyze how the framing and presence of explanations affect user trust and model performance. Overall, we provide evidence that adding an explanation in the model response to justify its reasoning significantly increases self-reported user trust in the model when the user has the opportun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02018","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/2406.02018/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":"2406.02018","created_at":"2026-07-05T09:15:58.105648+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.02018v2","created_at":"2026-07-05T09:15:58.105648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02018","created_at":"2026-07-05T09:15:58.105648+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJLTGXHDQJZU","created_at":"2026-07-05T09:15:58.105648+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJLTGXHDQJZU3SXX","created_at":"2026-07-05T09:15:58.105648+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJLTGXHD","created_at":"2026-07-05T09:15:58.105648+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15455","citing_title":"Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB","json":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB.json","graph_json":"https://pith.science/api/pith-number/EJLTGXHDQJZU3SXXXAJUUMUSAB/graph.json","events_json":"https://pith.science/api/pith-number/EJLTGXHDQJZU3SXXXAJUUMUSAB/events.json","paper":"https://pith.science/paper/EJLTGXHD"},"agent_actions":{"view_html":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB","download_json":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB.json","view_paper":"https://pith.science/paper/EJLTGXHD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.02018&json=true","fetch_graph":"https://pith.science/api/pith-number/EJLTGXHDQJZU3SXXXAJUUMUSAB/graph.json","fetch_events":"https://pith.science/api/pith-number/EJLTGXHDQJZU3SXXXAJUUMUSAB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB/action/storage_attestation","attest_author":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB/action/author_attestation","sign_citation":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB/action/citation_signature","submit_replication":"https://pith.science/pith/EJLTGXHDQJZU3SXXXAJUUMUSAB/action/replication_record"}},"created_at":"2026-07-05T09:15:58.105648+00:00","updated_at":"2026-07-05T09:15:58.105648+00:00"}