{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:FTPYYM5AXPGX47LOAWTGO6YXT7","short_pith_number":"pith:FTPYYM5A","canonical_record":{"source":{"id":"2311.08324","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T17:09:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc0ce47928411729972fe976e28e9a715940e7872a028b497b4635fd2f5df439","abstract_canon_sha256":"2ec37965aa272787b226d5332e95e49176a509ab96da08cac48e5f715e21ef23"},"schema_version":"1.0"},"canonical_sha256":"2cdf8c33a0bbcd7e7d6e05a6677b179ffdcd2ed262f2ff1ded6432eb8d0e9678","source":{"kind":"arxiv","id":"2311.08324","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.08324","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"2311.08324v2","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08324","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"FTPYYM5AXPGX","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_16","alias_value":"FTPYYM5AXPGX47LO","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_8","alias_value":"FTPYYM5A","created_at":"2026-07-05T08:03:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:FTPYYM5AXPGX47LOAWTGO6YXT7","target":"record","payload":{"canonical_record":{"source":{"id":"2311.08324","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T17:09:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc0ce47928411729972fe976e28e9a715940e7872a028b497b4635fd2f5df439","abstract_canon_sha256":"2ec37965aa272787b226d5332e95e49176a509ab96da08cac48e5f715e21ef23"},"schema_version":"1.0"},"canonical_sha256":"2cdf8c33a0bbcd7e7d6e05a6677b179ffdcd2ed262f2ff1ded6432eb8d0e9678","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:45.759673Z","signature_b64":"8/IJTrnW2U4mnqZ+OG328UpZnRgi9cmlBi89s0MrM9Re3nPDPeFDSe/Zeb8MGGW4TeeTeeWGI6x8Ra6vDff+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cdf8c33a0bbcd7e7d6e05a6677b179ffdcd2ed262f2ff1ded6432eb8d0e9678","last_reissued_at":"2026-07-05T08:03:45.759124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:45.759124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.08324","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4tRedXHVpVCbrhx7QQyJVEhyTsYPA3M+p7LpUovgMm43sdc1acSyA4cMo7nW/+w9kkq8VsTGF1/zUyc7QPtxDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:48:42.477205Z"},"content_sha256":"4e64e7d5b89403f24d7a2f6a85ffdb1ffb69360086c0445718d1fbe29e37dcc5","schema_version":"1.0","event_id":"sha256:4e64e7d5b89403f24d7a2f6a85ffdb1ffb69360086c0445718d1fbe29e37dcc5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:FTPYYM5AXPGX47LOAWTGO6YXT7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anti-LM Decoding for Zero-shot In-context Machine Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alexandra DeLucia, Kevin Duh, Suzanna Sia","submitted_at":"2023-11-14T17:09:43Z","abstract_excerpt":"Zero-shot In-context learning is the phenomenon where models can perform the task simply given the instructions. However, pre-trained large language models are known to be poorly calibrated for this task. One of the most effective approaches to handling this bias is to adopt a contrastive decoding objective, which accounts for the prior probability of generating the next token by conditioning on some context. This work introduces an Anti-Language Model objective with a decay factor designed to address the weaknesses of In-context Machine Translation. We conduct our experiments across 3 model t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08324","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/2311.08324/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":"2026-07-05T08:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iB/D/TpjQkLLOD9lw6iZeD9uHYnCilHIAFgOtvEQ+GY+/HapM9CIRmONK2xoKD2qL+2+uZwdQ8XaEGp2H12kDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:48:42.477790Z"},"content_sha256":"7b2d8c90ce41316b2755d4ea0aa39c4cd172730ccc976bf60c245a5596e2a912","schema_version":"1.0","event_id":"sha256:7b2d8c90ce41316b2755d4ea0aa39c4cd172730ccc976bf60c245a5596e2a912"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/bundle.json","state_url":"https://pith.science/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/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":"2026-08-21T17:48:42Z","links":{"resolver":"https://pith.science/pith/FTPYYM5AXPGX47LOAWTGO6YXT7","bundle":"https://pith.science/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/bundle.json","state":"https://pith.science/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FTPYYM5AXPGX47LOAWTGO6YXT7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:FTPYYM5AXPGX47LOAWTGO6YXT7","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":"2ec37965aa272787b226d5332e95e49176a509ab96da08cac48e5f715e21ef23","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T17:09:43Z","title_canon_sha256":"bc0ce47928411729972fe976e28e9a715940e7872a028b497b4635fd2f5df439"},"schema_version":"1.0","source":{"id":"2311.08324","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.08324","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"2311.08324v2","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08324","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"FTPYYM5AXPGX","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_16","alias_value":"FTPYYM5AXPGX47LO","created_at":"2026-07-05T08:03:45Z"},{"alias_kind":"pith_short_8","alias_value":"FTPYYM5A","created_at":"2026-07-05T08:03:45Z"}],"graph_snapshots":[{"event_id":"sha256:7b2d8c90ce41316b2755d4ea0aa39c4cd172730ccc976bf60c245a5596e2a912","target":"graph","created_at":"2026-07-05T08:03:45Z","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/2311.08324/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Zero-shot In-context learning is the phenomenon where models can perform the task simply given the instructions. However, pre-trained large language models are known to be poorly calibrated for this task. One of the most effective approaches to handling this bias is to adopt a contrastive decoding objective, which accounts for the prior probability of generating the next token by conditioning on some context. This work introduces an Anti-Language Model objective with a decay factor designed to address the weaknesses of In-context Machine Translation. We conduct our experiments across 3 model t","authors_text":"Alexandra DeLucia, Kevin Duh, Suzanna Sia","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T17:09:43Z","title":"Anti-LM Decoding for Zero-shot In-context Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08324","kind":"arxiv","version":2},"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":"sha256:4e64e7d5b89403f24d7a2f6a85ffdb1ffb69360086c0445718d1fbe29e37dcc5","target":"record","created_at":"2026-07-05T08:03:45Z","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":"2ec37965aa272787b226d5332e95e49176a509ab96da08cac48e5f715e21ef23","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T17:09:43Z","title_canon_sha256":"bc0ce47928411729972fe976e28e9a715940e7872a028b497b4635fd2f5df439"},"schema_version":"1.0","source":{"id":"2311.08324","kind":"arxiv","version":2}},"canonical_sha256":"2cdf8c33a0bbcd7e7d6e05a6677b179ffdcd2ed262f2ff1ded6432eb8d0e9678","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2cdf8c33a0bbcd7e7d6e05a6677b179ffdcd2ed262f2ff1ded6432eb8d0e9678","first_computed_at":"2026-07-05T08:03:45.759124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:45.759124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8/IJTrnW2U4mnqZ+OG328UpZnRgi9cmlBi89s0MrM9Re3nPDPeFDSe/Zeb8MGGW4TeeTeeWGI6x8Ra6vDff+AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:45.759673Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.08324","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e64e7d5b89403f24d7a2f6a85ffdb1ffb69360086c0445718d1fbe29e37dcc5","sha256:7b2d8c90ce41316b2755d4ea0aa39c4cd172730ccc976bf60c245a5596e2a912"],"state_sha256":"8cebdf1457dd7688effe339d0f576264beac2874aceb0f659c6dd209bad2459b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AmPfOZ4HK6VX/T1t6HHek5bO42R+fkQASK7m+itwyQY47bgnA4cfaExWllKJUlY0D22wEr0sMJrUuytzMIWjBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T17:48:42.482676Z","bundle_sha256":"ebc69f174fd8b538da458d2a6d9e710e58351934f429f8c9845b20b842a8495d"}}