{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:E57ZIFJQHL2GXE4TMK3MSXTI75","short_pith_number":"pith:E57ZIFJQ","schema_version":"1.0","canonical_sha256":"277f9415303af46b939362b6c95e68ff7826e69ad2c6b05c9f3843690646f2a9","source":{"kind":"arxiv","id":"2311.09606","version":2},"attestation_state":"computed","paper":{"title":"GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Clemens Rosenbaum, Ethan R. Elenberg, Shivanshu Gupta","submitted_at":"2023-11-16T06:28:05Z","abstract_excerpt":"In-context Learning (ICL) is the ability of Large Language Models (LLMs) to perform new tasks when conditioned on prompts comprising a few task examples. However, ICL performance can be critically sensitive to the choice of examples. To dynamically select the best examples for every test input, we propose Example Gisting, a novel approach for training example encoders through supervised fine-tuning with an attention bottleneck between the inputs and outputs. These gist models form the basis for GistScore, a novel metric for scoring and selecting informative examples. Further, we experiment wit"},"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":"2311.09606","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-16T06:28:05Z","cross_cats_sorted":[],"title_canon_sha256":"d21a291236aeb2a7fb4b8dd3330cf93108628bb0632b47d5349f56735823efc4","abstract_canon_sha256":"85d62e055393c9a6085c8ef176aa172fe27b9599d60ae8c5b560953fe8c9905f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:48:01.425877Z","signature_b64":"x6IF2D2Ge0ngxUIyLMiw+ugGUgPxZaDiILIM3AzIY75fZ3VqjbCvsN/xj8Fl9TEdrN4nBZEqwrwjGy+D2sDzDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"277f9415303af46b939362b6c95e68ff7826e69ad2c6b05c9f3843690646f2a9","last_reissued_at":"2026-07-05T07:48:01.425460Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:48:01.425460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Clemens Rosenbaum, Ethan R. Elenberg, Shivanshu Gupta","submitted_at":"2023-11-16T06:28:05Z","abstract_excerpt":"In-context Learning (ICL) is the ability of Large Language Models (LLMs) to perform new tasks when conditioned on prompts comprising a few task examples. However, ICL performance can be critically sensitive to the choice of examples. To dynamically select the best examples for every test input, we propose Example Gisting, a novel approach for training example encoders through supervised fine-tuning with an attention bottleneck between the inputs and outputs. These gist models form the basis for GistScore, a novel metric for scoring and selecting informative examples. Further, we experiment wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09606","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/2311.09606/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":"2311.09606","created_at":"2026-07-05T07:48:01.425523+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.09606v2","created_at":"2026-07-05T07:48:01.425523+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09606","created_at":"2026-07-05T07:48:01.425523+00:00"},{"alias_kind":"pith_short_12","alias_value":"E57ZIFJQHL2G","created_at":"2026-07-05T07:48:01.425523+00:00"},{"alias_kind":"pith_short_16","alias_value":"E57ZIFJQHL2GXE4T","created_at":"2026-07-05T07:48:01.425523+00:00"},{"alias_kind":"pith_short_8","alias_value":"E57ZIFJQ","created_at":"2026-07-05T07:48:01.425523+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75","json":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75.json","graph_json":"https://pith.science/api/pith-number/E57ZIFJQHL2GXE4TMK3MSXTI75/graph.json","events_json":"https://pith.science/api/pith-number/E57ZIFJQHL2GXE4TMK3MSXTI75/events.json","paper":"https://pith.science/paper/E57ZIFJQ"},"agent_actions":{"view_html":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75","download_json":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75.json","view_paper":"https://pith.science/paper/E57ZIFJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.09606&json=true","fetch_graph":"https://pith.science/api/pith-number/E57ZIFJQHL2GXE4TMK3MSXTI75/graph.json","fetch_events":"https://pith.science/api/pith-number/E57ZIFJQHL2GXE4TMK3MSXTI75/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75/action/storage_attestation","attest_author":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75/action/author_attestation","sign_citation":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75/action/citation_signature","submit_replication":"https://pith.science/pith/E57ZIFJQHL2GXE4TMK3MSXTI75/action/replication_record"}},"created_at":"2026-07-05T07:48:01.425523+00:00","updated_at":"2026-07-05T07:48:01.425523+00:00"}