{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HKNHDLU6B3I2IITUJ4ZFKHK2G2","short_pith_number":"pith:HKNHDLU6","schema_version":"1.0","canonical_sha256":"3a9a71ae9e0ed1a422744f32551d5a36ad75241def42b7b2e2fa6b32fd281736","source":{"kind":"arxiv","id":"2305.11554","version":4},"attestation_state":"computed","paper":{"title":"ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Shibo Hao, Tianyang Liu, Zhen Wang, Zhiting Hu","submitted_at":"2023-05-19T09:54:21Z","abstract_excerpt":"Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems. However, traditional methods, which finetune LLMs with tool demonstration data, can be both costly and restricted to a predefined set of tools. Recent in-context learning paradigm alleviates these issues, but the limited context length only allows for a few shots of demonstrations, leading to suboptimal understandings of the tools. Moreover, when there are numerous tools to choose from, in-context learning could completely fail to work. In this paper, we propose an alter"},"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":"2305.11554","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T09:54:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1d97da5071227804744a921755a305a2ebd0039a3855cb783e124053fff641ed","abstract_canon_sha256":"dd4744f586858f9fedb3b483cf26297484f8845e6d162018eb934cb0ed8762df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:33:50.950478Z","signature_b64":"xaxZ7AEbc7MH7U7m5PkXqpf6AUlNciVoIUFQ+HZTGBH5XxiuTOmBb59eyMevPXMrbRUKIXwnMYCwz+ZZRR0fCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a9a71ae9e0ed1a422744f32551d5a36ad75241def42b7b2e2fa6b32fd281736","last_reissued_at":"2026-07-05T07:33:50.950029Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:33:50.950029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Shibo Hao, Tianyang Liu, Zhen Wang, Zhiting Hu","submitted_at":"2023-05-19T09:54:21Z","abstract_excerpt":"Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems. However, traditional methods, which finetune LLMs with tool demonstration data, can be both costly and restricted to a predefined set of tools. Recent in-context learning paradigm alleviates these issues, but the limited context length only allows for a few shots of demonstrations, leading to suboptimal understandings of the tools. Moreover, when there are numerous tools to choose from, in-context learning could completely fail to work. In this paper, we propose an alter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11554","kind":"arxiv","version":4},"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/2305.11554/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":"2305.11554","created_at":"2026-07-05T07:33:50.950083+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.11554v4","created_at":"2026-07-05T07:33:50.950083+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11554","created_at":"2026-07-05T07:33:50.950083+00:00"},{"alias_kind":"pith_short_12","alias_value":"HKNHDLU6B3I2","created_at":"2026-07-05T07:33:50.950083+00:00"},{"alias_kind":"pith_short_16","alias_value":"HKNHDLU6B3I2IITU","created_at":"2026-07-05T07:33:50.950083+00:00"},{"alias_kind":"pith_short_8","alias_value":"HKNHDLU6","created_at":"2026-07-05T07:33:50.950083+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2307.06435","citing_title":"A Comprehensive Overview of Large Language Models","ref_index":220,"is_internal_anchor":false},{"citing_arxiv_id":"2304.08244","citing_title":"API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2306.06070","citing_title":"Mind2Web: Towards a Generalist Agent for the Web","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2","json":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2.json","graph_json":"https://pith.science/api/pith-number/HKNHDLU6B3I2IITUJ4ZFKHK2G2/graph.json","events_json":"https://pith.science/api/pith-number/HKNHDLU6B3I2IITUJ4ZFKHK2G2/events.json","paper":"https://pith.science/paper/HKNHDLU6"},"agent_actions":{"view_html":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2","download_json":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2.json","view_paper":"https://pith.science/paper/HKNHDLU6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.11554&json=true","fetch_graph":"https://pith.science/api/pith-number/HKNHDLU6B3I2IITUJ4ZFKHK2G2/graph.json","fetch_events":"https://pith.science/api/pith-number/HKNHDLU6B3I2IITUJ4ZFKHK2G2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2/action/storage_attestation","attest_author":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2/action/author_attestation","sign_citation":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2/action/citation_signature","submit_replication":"https://pith.science/pith/HKNHDLU6B3I2IITUJ4ZFKHK2G2/action/replication_record"}},"created_at":"2026-07-05T07:33:50.950083+00:00","updated_at":"2026-07-05T07:33:50.950083+00:00"}