{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:IMIT2WLPK374SENOT4DT4J2YKB","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":"7f264825e21819ec3a39022da21ffef62fb8d6e42f2aea0dc03e3d4e7a001cfe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T11:52:55Z","title_canon_sha256":"da1204b3aef0c2238500008d54f0c690c57e77043210b31c0bb73d1958181691"},"schema_version":"1.0","source":{"id":"2305.15065","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15065","created_at":"2026-07-05T07:20:51Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15065v2","created_at":"2026-07-05T07:20:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15065","created_at":"2026-07-05T07:20:51Z"},{"alias_kind":"pith_short_12","alias_value":"IMIT2WLPK374","created_at":"2026-07-05T07:20:51Z"},{"alias_kind":"pith_short_16","alias_value":"IMIT2WLPK374SENO","created_at":"2026-07-05T07:20:51Z"},{"alias_kind":"pith_short_8","alias_value":"IMIT2WLP","created_at":"2026-07-05T07:20:51Z"}],"graph_snapshots":[{"event_id":"sha256:97c4a779b7a93a911ce54912c7a93ccb8d7d31d20d1bc5ccd591b7b4b94c71bd","target":"graph","created_at":"2026-07-05T07:20:51Z","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/2305.15065/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited. Directly fine-tuning such language models can be effective for tailoring them, but it can be either extremely costly (e.g., GPT-3) or not even feasible for the broader community (e.g., GPT-4).\n  We propose Inference-time Policy Adapters (IPA), which efficiently tailors a language model such as GPT-3 without fine-tuning it. IPA guides a large base model during decoding time through a lightweigh","authors_text":"Abhilasha Ravichander, Bill Yuchen Lin, Faeze Brahman, Jaehun Jang, Jillian Fisher, Khyathi Chandu, Lianhui Qin, Liwei Jiang, Nouha Dziri, Peter West, Prithviraj Ammanabrolu, Sahana Ramnath, Sean Welleck, Skyler Hallinan, Xiang Ren, Ximing Lu, Yejin Choi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T11:52:55Z","title":"Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15065","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":"sha256:7f20afcb8fdd6d153cf8269b855d8ac1bd979026db6dedbe208f3ad085821310","target":"record","created_at":"2026-07-05T07:20:51Z","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":"7f264825e21819ec3a39022da21ffef62fb8d6e42f2aea0dc03e3d4e7a001cfe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T11:52:55Z","title_canon_sha256":"da1204b3aef0c2238500008d54f0c690c57e77043210b31c0bb73d1958181691"},"schema_version":"1.0","source":{"id":"2305.15065","kind":"arxiv","version":2}},"canonical_sha256":"43113d596f56ffc911ae9f073e27585043acb26ad9a86606508e595188402820","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43113d596f56ffc911ae9f073e27585043acb26ad9a86606508e595188402820","first_computed_at":"2026-07-05T07:20:51.220961Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:20:51.220961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XgVOwmGh1bFjuxLbpgFzRJgKo69uCEqQdL7WZ/ci4fgY7OSz5pPOGBwsbFgq3yRgxgrerG+Pme9FDTsQncAHDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:20:51.221398Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.15065","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f20afcb8fdd6d153cf8269b855d8ac1bd979026db6dedbe208f3ad085821310","sha256:97c4a779b7a93a911ce54912c7a93ccb8d7d31d20d1bc5ccd591b7b4b94c71bd"],"state_sha256":"72be58c6ba8162a05144a6010f204e6aa5ab879258d2675b918c79f933688c30"}