{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PFK3VZD3O4HO7CZEBKVZNYFGYN","short_pith_number":"pith:PFK3VZD3","schema_version":"1.0","canonical_sha256":"7955bae47b770eef8b240aab96e0a6c3734ae3862867485e491c65f800394793","source":{"kind":"arxiv","id":"2412.03096","version":2},"attestation_state":"computed","paper":{"title":"TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daling Wang, Huiying Cao, Shi Feng, Xiaocui Yang, Yifei Zhang, Yiqun Zhang","submitted_at":"2024-12-04T07:50:17Z","abstract_excerpt":"Empathetic conversation is a crucial characteristic in daily conversations between individuals. Nowadays, Large Language models (LLMs) have shown outstanding performance in generating empathetic responses. Knowledge bases like COMET can assist LLMs in mitigating illusions and enhancing the understanding of users' intentions and emotions. However, models remain heavily reliant on fixed knowledge bases and unrestricted incorporation of external knowledge can introduce noise. Tool learning is a flexible end-to-end approach that assists LLMs in handling complex problems. In this paper, we propose "},"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":"2412.03096","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T07:50:17Z","cross_cats_sorted":[],"title_canon_sha256":"839891fcac571b658542002ff7a2e691aa08c61e755252ef04b425cdce4905ce","abstract_canon_sha256":"c5d4812516f9cbb9e037824eaf63898c44ea0db271387020234dba97e35b2f8e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:59.801362Z","signature_b64":"Yf7HUvlX7oicSD4kQMc5/eWNbuRGcB3opR+zWX6hYunVJOJUjOQqeZZPbi+rWwFnDXLo0N2nbXzwt1xatiLlBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7955bae47b770eef8b240aab96e0a6c3734ae3862867485e491c65f800394793","last_reissued_at":"2026-07-05T09:45:59.800809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:59.800809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daling Wang, Huiying Cao, Shi Feng, Xiaocui Yang, Yifei Zhang, Yiqun Zhang","submitted_at":"2024-12-04T07:50:17Z","abstract_excerpt":"Empathetic conversation is a crucial characteristic in daily conversations between individuals. Nowadays, Large Language models (LLMs) have shown outstanding performance in generating empathetic responses. Knowledge bases like COMET can assist LLMs in mitigating illusions and enhancing the understanding of users' intentions and emotions. However, models remain heavily reliant on fixed knowledge bases and unrestricted incorporation of external knowledge can introduce noise. Tool learning is a flexible end-to-end approach that assists LLMs in handling complex problems. In this paper, we propose "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03096","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/2412.03096/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":"2412.03096","created_at":"2026-07-05T09:45:59.800871+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.03096v2","created_at":"2026-07-05T09:45:59.800871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03096","created_at":"2026-07-05T09:45:59.800871+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFK3VZD3O4HO","created_at":"2026-07-05T09:45:59.800871+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFK3VZD3O4HO7CZE","created_at":"2026-07-05T09:45:59.800871+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFK3VZD3","created_at":"2026-07-05T09:45:59.800871+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/PFK3VZD3O4HO7CZEBKVZNYFGYN","json":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN.json","graph_json":"https://pith.science/api/pith-number/PFK3VZD3O4HO7CZEBKVZNYFGYN/graph.json","events_json":"https://pith.science/api/pith-number/PFK3VZD3O4HO7CZEBKVZNYFGYN/events.json","paper":"https://pith.science/paper/PFK3VZD3"},"agent_actions":{"view_html":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN","download_json":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN.json","view_paper":"https://pith.science/paper/PFK3VZD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.03096&json=true","fetch_graph":"https://pith.science/api/pith-number/PFK3VZD3O4HO7CZEBKVZNYFGYN/graph.json","fetch_events":"https://pith.science/api/pith-number/PFK3VZD3O4HO7CZEBKVZNYFGYN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN/action/storage_attestation","attest_author":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN/action/author_attestation","sign_citation":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN/action/citation_signature","submit_replication":"https://pith.science/pith/PFK3VZD3O4HO7CZEBKVZNYFGYN/action/replication_record"}},"created_at":"2026-07-05T09:45:59.800871+00:00","updated_at":"2026-07-05T09:45:59.800871+00:00"}