{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KBVUO45G23ASMGESN7GRBXTUYH","short_pith_number":"pith:KBVUO45G","canonical_record":{"source":{"id":"2605.28058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T07:04:39Z","cross_cats_sorted":[],"title_canon_sha256":"db422b3ac0269bdccf60e6553f1b79813e3d2da6d6920edf0652ed2e568044aa","abstract_canon_sha256":"cc9852d8fe5dd1f2c814c5f0f17067cb9481e1f4ca5258d2ff09fe86ab38aeeb"},"schema_version":"1.0"},"canonical_sha256":"506b4773a6d6c12618926fcd10de74c1efa27d6c89d6c2faf68bdc371dcc704a","source":{"kind":"arxiv","id":"2605.28058","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28058","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28058v1","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28058","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"KBVUO45G23AS","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"KBVUO45G23ASMGES","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"KBVUO45G","created_at":"2026-05-28T01:04:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KBVUO45G23ASMGESN7GRBXTUYH","target":"record","payload":{"canonical_record":{"source":{"id":"2605.28058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T07:04:39Z","cross_cats_sorted":[],"title_canon_sha256":"db422b3ac0269bdccf60e6553f1b79813e3d2da6d6920edf0652ed2e568044aa","abstract_canon_sha256":"cc9852d8fe5dd1f2c814c5f0f17067cb9481e1f4ca5258d2ff09fe86ab38aeeb"},"schema_version":"1.0"},"canonical_sha256":"506b4773a6d6c12618926fcd10de74c1efa27d6c89d6c2faf68bdc371dcc704a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:57.329536Z","signature_b64":"FT8eHcqEOsEN3H7BTNXtkAzcB4/zL7OpX5qzY4h77aY8y2DvDb6axKkUmaiYQP5wmYs9OQnd2CybxMoKs7Y1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"506b4773a6d6c12618926fcd10de74c1efa27d6c89d6c2faf68bdc371dcc704a","last_reissued_at":"2026-05-28T01:04:57.329111Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:57.329111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.28058","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":"2026-05-28T01:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9RSEFU3Ez/prwg3TNQl/sokKfz4jFvrjVPmwTJqX+GqqcA6QrqGZuYcDu7RnicyzviTV69e+dipV1GTB9Wg1DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T21:04:01.677156Z"},"content_sha256":"4b4c2ae2f62f571c05242355f99175e037fcd2235b09431ef9208f70ac5952bc","schema_version":"1.0","event_id":"sha256:4b4c2ae2f62f571c05242355f99175e037fcd2235b09431ef9208f70ac5952bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KBVUO45G23ASMGESN7GRBXTUYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Christian Wolff, Jakob Fehle, Niklas Donhauser, Nils Constantin Hellwig, Udo Kruschwitz","submitted_at":"2026-05-27T07:04:39Z","abstract_excerpt":"Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring substantially fewer annotated examples while achieving notable improvements over zero-shot baselines. However, a performance gap remained compared to models fine-tuned on hundreds of examples, and the computational costs of LLM inference present practical barriers to deployment. We introduce LLM-based Multi-View Prompting (LLM-MvP), which adapts the multi-view principle of considering multiple element orderings to LLM prompting. By combining sche"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28058","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/2605.28058/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":"2026-05-28T01:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lFoqPyb+uyqsswmVou5hFmGUpAxUjUbuxJO/PgWH0ik3tTgDhsd3/0Gs2Z51R5EO6klnMBtZp8gnXL/aJe2yAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T21:04:01.677923Z"},"content_sha256":"0a10c13be87b99b1973e932ce67207b490fac0bf7306010adf1909652944bd07","schema_version":"1.0","event_id":"sha256:0a10c13be87b99b1973e932ce67207b490fac0bf7306010adf1909652944bd07"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KBVUO45G23ASMGESN7GRBXTUYH/bundle.json","state_url":"https://pith.science/pith/KBVUO45G23ASMGESN7GRBXTUYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KBVUO45G23ASMGESN7GRBXTUYH/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":"2026-06-07T21:04:01Z","links":{"resolver":"https://pith.science/pith/KBVUO45G23ASMGESN7GRBXTUYH","bundle":"https://pith.science/pith/KBVUO45G23ASMGESN7GRBXTUYH/bundle.json","state":"https://pith.science/pith/KBVUO45G23ASMGESN7GRBXTUYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KBVUO45G23ASMGESN7GRBXTUYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KBVUO45G23ASMGESN7GRBXTUYH","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":"cc9852d8fe5dd1f2c814c5f0f17067cb9481e1f4ca5258d2ff09fe86ab38aeeb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T07:04:39Z","title_canon_sha256":"db422b3ac0269bdccf60e6553f1b79813e3d2da6d6920edf0652ed2e568044aa"},"schema_version":"1.0","source":{"id":"2605.28058","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28058","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28058v1","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28058","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"KBVUO45G23AS","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"KBVUO45G23ASMGES","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"KBVUO45G","created_at":"2026-05-28T01:04:57Z"}],"graph_snapshots":[{"event_id":"sha256:0a10c13be87b99b1973e932ce67207b490fac0bf7306010adf1909652944bd07","target":"graph","created_at":"2026-05-28T01:04:57Z","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/2605.28058/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring substantially fewer annotated examples while achieving notable improvements over zero-shot baselines. However, a performance gap remained compared to models fine-tuned on hundreds of examples, and the computational costs of LLM inference present practical barriers to deployment. We introduce LLM-based Multi-View Prompting (LLM-MvP), which adapts the multi-view principle of considering multiple element orderings to LLM prompting. By combining sche","authors_text":"Christian Wolff, Jakob Fehle, Niklas Donhauser, Nils Constantin Hellwig, Udo Kruschwitz","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T07:04:39Z","title":"Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28058","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":"sha256:4b4c2ae2f62f571c05242355f99175e037fcd2235b09431ef9208f70ac5952bc","target":"record","created_at":"2026-05-28T01:04:57Z","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":"cc9852d8fe5dd1f2c814c5f0f17067cb9481e1f4ca5258d2ff09fe86ab38aeeb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T07:04:39Z","title_canon_sha256":"db422b3ac0269bdccf60e6553f1b79813e3d2da6d6920edf0652ed2e568044aa"},"schema_version":"1.0","source":{"id":"2605.28058","kind":"arxiv","version":1}},"canonical_sha256":"506b4773a6d6c12618926fcd10de74c1efa27d6c89d6c2faf68bdc371dcc704a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"506b4773a6d6c12618926fcd10de74c1efa27d6c89d6c2faf68bdc371dcc704a","first_computed_at":"2026-05-28T01:04:57.329111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:57.329111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FT8eHcqEOsEN3H7BTNXtkAzcB4/zL7OpX5qzY4h77aY8y2DvDb6axKkUmaiYQP5wmYs9OQnd2CybxMoKs7Y1Cw==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:57.329536Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.28058","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4b4c2ae2f62f571c05242355f99175e037fcd2235b09431ef9208f70ac5952bc","sha256:0a10c13be87b99b1973e932ce67207b490fac0bf7306010adf1909652944bd07"],"state_sha256":"5fd22151f1d1e8e07402f2e12e58aa86df8de214961eb96d7d089479da4b09f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rBVCJAwBqlnjqnMnn52Wip8LYplf8qbW6C9Pq3HUsvsuU4u/bGFcjYEJnydrF2LUN+sCiTh2j8v3VzXESIRRDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T21:04:01.682562Z","bundle_sha256":"c6de5b783bccf536a38306480fb9a7a0cd149aeba7fcfd0ceb7c9ec744cf11af"}}