{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:UZQXN5UT5ANLMQ5YD3XZFGYVRF","short_pith_number":"pith:UZQXN5UT","canonical_record":{"source":{"id":"2210.06726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-13T04:50:02Z","cross_cats_sorted":[],"title_canon_sha256":"aaf9238538f307edac02b509e8c57e17a77a184f50d1364ac06e5392a5a3a001","abstract_canon_sha256":"0006c063d5354527315b404cbad8f90160fbc02b2eb1ec8fb7e1b8c22c16d62a"},"schema_version":"1.0"},"canonical_sha256":"a66176f693e81ab643b81eef929b15895c1466f00b9a0b685cfef94d2be52a67","source":{"kind":"arxiv","id":"2210.06726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.06726","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"2210.06726v1","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.06726","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"UZQXN5UT5ANL","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_16","alias_value":"UZQXN5UT5ANLMQ5Y","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_8","alias_value":"UZQXN5UT","created_at":"2026-07-05T05:06:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:UZQXN5UT5ANLMQ5YD3XZFGYVRF","target":"record","payload":{"canonical_record":{"source":{"id":"2210.06726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-13T04:50:02Z","cross_cats_sorted":[],"title_canon_sha256":"aaf9238538f307edac02b509e8c57e17a77a184f50d1364ac06e5392a5a3a001","abstract_canon_sha256":"0006c063d5354527315b404cbad8f90160fbc02b2eb1ec8fb7e1b8c22c16d62a"},"schema_version":"1.0"},"canonical_sha256":"a66176f693e81ab643b81eef929b15895c1466f00b9a0b685cfef94d2be52a67","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:06:19.759462Z","signature_b64":"NqJlkN+iC7p5zJNLRRjj/gc4dpWRVWsR65n1bhAMkHamPZ4yPqt8xlOuu2qtP5oIxPVA3+xLWuzZ2gvc2cxQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a66176f693e81ab643b81eef929b15895c1466f00b9a0b685cfef94d2be52a67","last_reissued_at":"2026-07-05T05:06:19.758966Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:06:19.758966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.06726","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":"2026-07-05T05:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VFV6oINt5QQGFotZiB4zVRHVbgHZWbcM0RJsZCwq6aLDJYgKd/wAxXbiLAwkboO/2l6R8OWs4nYPZegUJc+dDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:49:58.027246Z"},"content_sha256":"b466ddd978c9a0b48bc75733eb9ae18374e5f4b5c25a854d943dfa71584c238e","schema_version":"1.0","event_id":"sha256:b466ddd978c9a0b48bc75733eb9ae18374e5f4b5c25a854d943dfa71584c238e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:UZQXN5UT5ANLMQ5YD3XZFGYVRF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explanations from Large Language Models Make Small Reasoners Better","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baolin Peng, Hong Wang, Jianshu Chen, Jing Qian, Shiyang Li, Wenhu Chen, Xifeng Yan, Xinlu Zhang, Yelong Shen, Yi Mao, Zekun Li, Zhiyu Chen","submitted_at":"2022-10-13T04:50:02Z","abstract_excerpt":"Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In this paper, we consider the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost. We systematically explore three explanation generation approaches from LLM and utilize a multi-task learning framework to facilitate small models to acquire strong reasoning power together with explanation generation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.06726","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/2210.06726/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":"2026-07-05T05:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p4D/vm9FK+imBm3ETnqCZovf8nVZXqCXzIvvpLspAoLH2rlrvBF/k6MuGBQvMgsmAIoj5jfBwPvS0CqPIL5fAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:49:58.027694Z"},"content_sha256":"f34875bad48498aa18f13873368dc4b060947aa531eedf09940bf5f59129f76a","schema_version":"1.0","event_id":"sha256:f34875bad48498aa18f13873368dc4b060947aa531eedf09940bf5f59129f76a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/bundle.json","state_url":"https://pith.science/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/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":"2026-08-06T20:49:58Z","links":{"resolver":"https://pith.science/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF","bundle":"https://pith.science/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/bundle.json","state":"https://pith.science/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZQXN5UT5ANLMQ5YD3XZFGYVRF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:UZQXN5UT5ANLMQ5YD3XZFGYVRF","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":"0006c063d5354527315b404cbad8f90160fbc02b2eb1ec8fb7e1b8c22c16d62a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-13T04:50:02Z","title_canon_sha256":"aaf9238538f307edac02b509e8c57e17a77a184f50d1364ac06e5392a5a3a001"},"schema_version":"1.0","source":{"id":"2210.06726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.06726","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"2210.06726v1","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.06726","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"UZQXN5UT5ANL","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_16","alias_value":"UZQXN5UT5ANLMQ5Y","created_at":"2026-07-05T05:06:19Z"},{"alias_kind":"pith_short_8","alias_value":"UZQXN5UT","created_at":"2026-07-05T05:06:19Z"}],"graph_snapshots":[{"event_id":"sha256:f34875bad48498aa18f13873368dc4b060947aa531eedf09940bf5f59129f76a","target":"graph","created_at":"2026-07-05T05:06:19Z","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/2210.06726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In this paper, we consider the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost. We systematically explore three explanation generation approaches from LLM and utilize a multi-task learning framework to facilitate small models to acquire strong reasoning power together with explanation generation ","authors_text":"Baolin Peng, Hong Wang, Jianshu Chen, Jing Qian, Shiyang Li, Wenhu Chen, Xifeng Yan, Xinlu Zhang, Yelong Shen, Yi Mao, Zekun Li, Zhiyu Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-13T04:50:02Z","title":"Explanations from Large Language Models Make Small Reasoners Better"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.06726","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:b466ddd978c9a0b48bc75733eb9ae18374e5f4b5c25a854d943dfa71584c238e","target":"record","created_at":"2026-07-05T05:06:19Z","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":"0006c063d5354527315b404cbad8f90160fbc02b2eb1ec8fb7e1b8c22c16d62a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-13T04:50:02Z","title_canon_sha256":"aaf9238538f307edac02b509e8c57e17a77a184f50d1364ac06e5392a5a3a001"},"schema_version":"1.0","source":{"id":"2210.06726","kind":"arxiv","version":1}},"canonical_sha256":"a66176f693e81ab643b81eef929b15895c1466f00b9a0b685cfef94d2be52a67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a66176f693e81ab643b81eef929b15895c1466f00b9a0b685cfef94d2be52a67","first_computed_at":"2026-07-05T05:06:19.758966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:06:19.758966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NqJlkN+iC7p5zJNLRRjj/gc4dpWRVWsR65n1bhAMkHamPZ4yPqt8xlOuu2qtP5oIxPVA3+xLWuzZ2gvc2cxQBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:06:19.759462Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.06726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b466ddd978c9a0b48bc75733eb9ae18374e5f4b5c25a854d943dfa71584c238e","sha256:f34875bad48498aa18f13873368dc4b060947aa531eedf09940bf5f59129f76a"],"state_sha256":"5ff1fdd62b063a71f9b1c5577381b6703dbd859790b4226ccbf99388c07ef0b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WUVk1Z34h4TJuq/hd6R2qsuuNC8qx3tA53sf7AKKlTxmk46iMe4OOTpoSQ89ViTqUcS2AJSXFCjyoWzQMK4lDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:49:58.030365Z","bundle_sha256":"fc30fc657fd2e900744679a087b642aff9b48ef73693c1f691a68d72253c4213"}}