{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:I7XTANKHLGPAOXZK3OBVG5WNJ6","short_pith_number":"pith:I7XTANKH","canonical_record":{"source":{"id":"2308.01240","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-02T15:54:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"10d4b6cb250a9b236370d4f65ad418cd838295de4086883a784f0a7a54942bb4","abstract_canon_sha256":"819a4a013e5189e74355c46fa18285fb3f8063e16c8ce220d751558bc49136b1"},"schema_version":"1.0"},"canonical_sha256":"47ef303547599e075f2adb835376cd4fbae10770bde5e791ffc7b4f54754295e","source":{"kind":"arxiv","id":"2308.01240","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01240","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01240v1","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01240","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_12","alias_value":"I7XTANKHLGPA","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_16","alias_value":"I7XTANKHLGPAOXZK","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_8","alias_value":"I7XTANKH","created_at":"2026-07-05T06:37:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:I7XTANKHLGPAOXZK3OBVG5WNJ6","target":"record","payload":{"canonical_record":{"source":{"id":"2308.01240","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-02T15:54:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"10d4b6cb250a9b236370d4f65ad418cd838295de4086883a784f0a7a54942bb4","abstract_canon_sha256":"819a4a013e5189e74355c46fa18285fb3f8063e16c8ce220d751558bc49136b1"},"schema_version":"1.0"},"canonical_sha256":"47ef303547599e075f2adb835376cd4fbae10770bde5e791ffc7b4f54754295e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:07.677911Z","signature_b64":"fpr5x7l98vvK0brL9oUvIm7ztIkpRbCYfXjWzxZCVWVocHEsFCNkteqDzMiY+c6F8NowOmosLdCeVxOatLjtAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47ef303547599e075f2adb835376cd4fbae10770bde5e791ffc7b4f54754295e","last_reissued_at":"2026-07-05T06:37:07.677476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:07.677476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.01240","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":"2026-07-05T06:37:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pTwrGTQT/3bgzs6VWGOP+/+IjQ8HmuNuA2/S6jcmgVWNgc3tgaLtzbPIPgVnn0MteDO9FHQRY+BMEab5xOoOCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T09:42:47.209625Z"},"content_sha256":"6b75c90426488b2013db8619dfc06af87fc761eaf2c47d852d801677fac11ca7","schema_version":"1.0","event_id":"sha256:6b75c90426488b2013db8619dfc06af87fc761eaf2c47d852d801677fac11ca7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:I7XTANKHLGPAOXZK3OBVG5WNJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Junwei Liu, Mingwei Liu, Qiancheng Zi, Xin Peng, Yiling Lou, Zhiqiang Yuan","submitted_at":"2023-08-02T15:54:22Z","abstract_excerpt":"In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01240","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/2308.01240/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":"2026-07-05T06:37:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TGqycdNcJiRfB1NyrMhYbhIBA/fF+6Myvz6I6NmpJNvxteGRsmXLntruCOvCVdOCho0U8UprozIxzD9BOb+fCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T09:42:47.210113Z"},"content_sha256":"e22e22b8967089b83a48ee580b638c138503541152b7016a1ba5ad5776a9a632","schema_version":"1.0","event_id":"sha256:e22e22b8967089b83a48ee580b638c138503541152b7016a1ba5ad5776a9a632"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/bundle.json","state_url":"https://pith.science/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/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":"2026-08-07T09:42:47Z","links":{"resolver":"https://pith.science/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6","bundle":"https://pith.science/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/bundle.json","state":"https://pith.science/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I7XTANKHLGPAOXZK3OBVG5WNJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:I7XTANKHLGPAOXZK3OBVG5WNJ6","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":"819a4a013e5189e74355c46fa18285fb3f8063e16c8ce220d751558bc49136b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-02T15:54:22Z","title_canon_sha256":"10d4b6cb250a9b236370d4f65ad418cd838295de4086883a784f0a7a54942bb4"},"schema_version":"1.0","source":{"id":"2308.01240","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01240","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01240v1","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01240","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_12","alias_value":"I7XTANKHLGPA","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_16","alias_value":"I7XTANKHLGPAOXZK","created_at":"2026-07-05T06:37:07Z"},{"alias_kind":"pith_short_8","alias_value":"I7XTANKH","created_at":"2026-07-05T06:37:07Z"}],"graph_snapshots":[{"event_id":"sha256:e22e22b8967089b83a48ee580b638c138503541152b7016a1ba5ad5776a9a632","target":"graph","created_at":"2026-07-05T06:37:07Z","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/2308.01240/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code com","authors_text":"Junwei Liu, Mingwei Liu, Qiancheng Zi, Xin Peng, Yiling Lou, Zhiqiang Yuan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-02T15:54:22Z","title":"Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01240","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":"sha256:6b75c90426488b2013db8619dfc06af87fc761eaf2c47d852d801677fac11ca7","target":"record","created_at":"2026-07-05T06:37:07Z","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":"819a4a013e5189e74355c46fa18285fb3f8063e16c8ce220d751558bc49136b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-02T15:54:22Z","title_canon_sha256":"10d4b6cb250a9b236370d4f65ad418cd838295de4086883a784f0a7a54942bb4"},"schema_version":"1.0","source":{"id":"2308.01240","kind":"arxiv","version":1}},"canonical_sha256":"47ef303547599e075f2adb835376cd4fbae10770bde5e791ffc7b4f54754295e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47ef303547599e075f2adb835376cd4fbae10770bde5e791ffc7b4f54754295e","first_computed_at":"2026-07-05T06:37:07.677476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:37:07.677476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fpr5x7l98vvK0brL9oUvIm7ztIkpRbCYfXjWzxZCVWVocHEsFCNkteqDzMiY+c6F8NowOmosLdCeVxOatLjtAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:37:07.677911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.01240","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b75c90426488b2013db8619dfc06af87fc761eaf2c47d852d801677fac11ca7","sha256:e22e22b8967089b83a48ee580b638c138503541152b7016a1ba5ad5776a9a632"],"state_sha256":"4e8beaab4878e72ff499dd43bba10c4f5ef0757f78489594ee652e42269c9eb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gkNuIX4IEFe+WqdThhbc0I4e0KLZXOCwYCDVQiQIYR90qvKIUad23OzPszVcLBtDg9IxPGkEr/hdc2ZtKcyHBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T09:42:47.215220Z","bundle_sha256":"e14630e01d68d12f70d609bbe4f6bf7d24283d05ac8751edc90522a20d4e0ff3"}}