{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QCZECMJGYQY4RRNAO2FZ6UUCBA","short_pith_number":"pith:QCZECMJG","canonical_record":{"source":{"id":"2409.16788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-25T09:52:44Z","cross_cats_sorted":[],"title_canon_sha256":"52e2c1b07cb407f9fb603e40bc7cf8a837672ba9e66e8eb525b3dcc2b2130648","abstract_canon_sha256":"fbec2543d23bf6fcb561e60235dcfd1f5ace7de94c8e82d5d015a4711249c0f6"},"schema_version":"1.0"},"canonical_sha256":"80b2413126c431c8c5a0768b9f5282081247e82c5bd1a431436aabe45b08373a","source":{"kind":"arxiv","id":"2409.16788","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.16788","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.16788v1","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16788","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_12","alias_value":"QCZECMJGYQY4","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_16","alias_value":"QCZECMJGYQY4RRNA","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_8","alias_value":"QCZECMJG","created_at":"2026-07-05T09:11:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QCZECMJGYQY4RRNAO2FZ6UUCBA","target":"record","payload":{"canonical_record":{"source":{"id":"2409.16788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-25T09:52:44Z","cross_cats_sorted":[],"title_canon_sha256":"52e2c1b07cb407f9fb603e40bc7cf8a837672ba9e66e8eb525b3dcc2b2130648","abstract_canon_sha256":"fbec2543d23bf6fcb561e60235dcfd1f5ace7de94c8e82d5d015a4711249c0f6"},"schema_version":"1.0"},"canonical_sha256":"80b2413126c431c8c5a0768b9f5282081247e82c5bd1a431436aabe45b08373a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:39.366327Z","signature_b64":"Dt34VFL2wS+NAZypIxpVMHHxwy3+emogHgVN0B9pTNlbObWs2LHj7OOCMBLoV8uJA4NDVKk7c88nj27m6QcADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80b2413126c431c8c5a0768b9f5282081247e82c5bd1a431436aabe45b08373a","last_reissued_at":"2026-07-05T09:11:39.365797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:39.365797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.16788","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":"2026-07-05T09:11:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9rJpRPyhl1PIigV+5piPu9oESlPo9qzSQrREMeOQekKIjw8X8JYr6EJHvegtbJmG7H2JSg4Q7qJTGrFx3kQnDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:52:51.704562Z"},"content_sha256":"4ca6094a940c17263cf266de592b495ed7f616abdbaec07fa44d678ebc7d177a","schema_version":"1.0","event_id":"sha256:4ca6094a940c17263cf266de592b495ed7f616abdbaec07fa44d678ebc7d177a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QCZECMJGYQY4RRNAO2FZ6UUCBA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mitigating the Bias of Large Language Model Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Xu, Conghui Zhu, Hailong Cao, Hongli Zhou, Hui Huang, Muyun Yang, Tiejun Zhao, Yunfei Long","submitted_at":"2024-09-25T09:52:44Z","abstract_excerpt":"Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output quality. However, existing judges are proven to be biased, namely they would favor answers which present better superficial quality (such as verbosity, fluency) while ignoring the instruction following ability. In this work, we propose systematic research about the bias of LLM-as-a-Judge. Specifically, for closed-source judge models, we apply calibration to mitigate the significance of superficial quality, both on pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16788","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/2409.16788/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":"2026-07-05T09:11:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X7uUKsxrPvcyYsfDD2RojedIsrFUZFOsOjU5Lvai4MfZIUbD+1Ys4d/un4PYoXOWxvlVMfA3/OJLKRs86G0dCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:52:51.705373Z"},"content_sha256":"955a7941f1a9dd6c486b702567941d572e94ebbd9e02a9c3aa1530fb2dde22de","schema_version":"1.0","event_id":"sha256:955a7941f1a9dd6c486b702567941d572e94ebbd9e02a9c3aa1530fb2dde22de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/bundle.json","state_url":"https://pith.science/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/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":"2026-08-04T12:52:51Z","links":{"resolver":"https://pith.science/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA","bundle":"https://pith.science/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/bundle.json","state":"https://pith.science/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QCZECMJGYQY4RRNAO2FZ6UUCBA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QCZECMJGYQY4RRNAO2FZ6UUCBA","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":"fbec2543d23bf6fcb561e60235dcfd1f5ace7de94c8e82d5d015a4711249c0f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-25T09:52:44Z","title_canon_sha256":"52e2c1b07cb407f9fb603e40bc7cf8a837672ba9e66e8eb525b3dcc2b2130648"},"schema_version":"1.0","source":{"id":"2409.16788","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.16788","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.16788v1","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16788","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_12","alias_value":"QCZECMJGYQY4","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_16","alias_value":"QCZECMJGYQY4RRNA","created_at":"2026-07-05T09:11:39Z"},{"alias_kind":"pith_short_8","alias_value":"QCZECMJG","created_at":"2026-07-05T09:11:39Z"}],"graph_snapshots":[{"event_id":"sha256:955a7941f1a9dd6c486b702567941d572e94ebbd9e02a9c3aa1530fb2dde22de","target":"graph","created_at":"2026-07-05T09:11:39Z","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/2409.16788/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output quality. However, existing judges are proven to be biased, namely they would favor answers which present better superficial quality (such as verbosity, fluency) while ignoring the instruction following ability. In this work, we propose systematic research about the bias of LLM-as-a-Judge. Specifically, for closed-source judge models, we apply calibration to mitigate the significance of superficial quality, both on pr","authors_text":"Bing Xu, Conghui Zhu, Hailong Cao, Hongli Zhou, Hui Huang, Muyun Yang, Tiejun Zhao, Yunfei Long","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-25T09:52:44Z","title":"Mitigating the Bias of Large Language Model Evaluation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16788","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":"sha256:4ca6094a940c17263cf266de592b495ed7f616abdbaec07fa44d678ebc7d177a","target":"record","created_at":"2026-07-05T09:11:39Z","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":"fbec2543d23bf6fcb561e60235dcfd1f5ace7de94c8e82d5d015a4711249c0f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-25T09:52:44Z","title_canon_sha256":"52e2c1b07cb407f9fb603e40bc7cf8a837672ba9e66e8eb525b3dcc2b2130648"},"schema_version":"1.0","source":{"id":"2409.16788","kind":"arxiv","version":1}},"canonical_sha256":"80b2413126c431c8c5a0768b9f5282081247e82c5bd1a431436aabe45b08373a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80b2413126c431c8c5a0768b9f5282081247e82c5bd1a431436aabe45b08373a","first_computed_at":"2026-07-05T09:11:39.365797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:11:39.365797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dt34VFL2wS+NAZypIxpVMHHxwy3+emogHgVN0B9pTNlbObWs2LHj7OOCMBLoV8uJA4NDVKk7c88nj27m6QcADg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:11:39.366327Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.16788","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ca6094a940c17263cf266de592b495ed7f616abdbaec07fa44d678ebc7d177a","sha256:955a7941f1a9dd6c486b702567941d572e94ebbd9e02a9c3aa1530fb2dde22de"],"state_sha256":"20a593b90db728adacfdef8a0e58c5bf23ace4a906a6a24c55563e488519daf4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XjZWV86Vn8UX0NsyIwv1b9n/urm/x+RlZiGP7gNZXNgoTwoE3WXpB1J+Izn2LcWDOf+Es7ItyaGXLgq1uVJbAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:52:51.711196Z","bundle_sha256":"0cb493e9e110a3ba503d360a543ef9173273afd9426747b06e01c4859e1778a7"}}