{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2CQSVXRZNXFHGSVSLT22T3E6MZ","short_pith_number":"pith:2CQSVXRZ","schema_version":"1.0","canonical_sha256":"d0a12ade396dca734ab25cf5a9ec9e667d828b79c63cc543c3daba739e1d298b","source":{"kind":"arxiv","id":"2402.05119","version":5},"attestation_state":"computed","paper":{"title":"A Closer Look at the Limitations of Instruction Tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chandra Kiran Reddy Evuru, Deepali Aneja, Dinesh Manocha, Ramaneswaran S, Ramani Duraiswami, Sonal Kumar, Sreyan Ghosh, Zeyu Jin","submitted_at":"2024-02-03T04:45:25Z","abstract_excerpt":"Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limite"},"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":"2402.05119","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T04:45:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"66d691b346c2e21cd81f7995fb2aff8e14638016bf26c2a7c0de154ae0f61c5e","abstract_canon_sha256":"3ed86314444d019ba958469ee6bb3d284c7f00d34d1ef281a383a378166c820f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:26.151685Z","signature_b64":"XoZ6AxFZkgPZFiXKL9iZ3J6Pizp+RN0WqwZq0+eZMFIhWcRX1Pq1pfhj3I6E3dM931fO3O2+/bn5DamSp0sZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0a12ade396dca734ab25cf5a9ec9e667d828b79c63cc543c3daba739e1d298b","last_reissued_at":"2026-07-05T08:43:26.151257Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:26.151257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Closer Look at the Limitations of Instruction Tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chandra Kiran Reddy Evuru, Deepali Aneja, Dinesh Manocha, Ramaneswaran S, Ramani Duraiswami, Sonal Kumar, Sreyan Ghosh, Zeyu Jin","submitted_at":"2024-02-03T04:45:25Z","abstract_excerpt":"Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limite"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05119","kind":"arxiv","version":5},"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/2402.05119/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":"2402.05119","created_at":"2026-07-05T08:43:26.151313+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.05119v5","created_at":"2026-07-05T08:43:26.151313+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05119","created_at":"2026-07-05T08:43:26.151313+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CQSVXRZNXFH","created_at":"2026-07-05T08:43:26.151313+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CQSVXRZNXFHGSVS","created_at":"2026-07-05T08:43:26.151313+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CQSVXRZ","created_at":"2026-07-05T08:43:26.151313+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04160","citing_title":"Expert-Aware Refusal Steering","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2512.12677","citing_title":"Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2504.21850","citing_title":"Visual Compositional Tuning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11157","citing_title":"Response-Based Knowledge Distillation for Multilingual Jailbreak Prevention Unwittingly Compromises Safety","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20148","citing_title":"Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ","json":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ.json","graph_json":"https://pith.science/api/pith-number/2CQSVXRZNXFHGSVSLT22T3E6MZ/graph.json","events_json":"https://pith.science/api/pith-number/2CQSVXRZNXFHGSVSLT22T3E6MZ/events.json","paper":"https://pith.science/paper/2CQSVXRZ"},"agent_actions":{"view_html":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ","download_json":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ.json","view_paper":"https://pith.science/paper/2CQSVXRZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.05119&json=true","fetch_graph":"https://pith.science/api/pith-number/2CQSVXRZNXFHGSVSLT22T3E6MZ/graph.json","fetch_events":"https://pith.science/api/pith-number/2CQSVXRZNXFHGSVSLT22T3E6MZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ/action/storage_attestation","attest_author":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ/action/author_attestation","sign_citation":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ/action/citation_signature","submit_replication":"https://pith.science/pith/2CQSVXRZNXFHGSVSLT22T3E6MZ/action/replication_record"}},"created_at":"2026-07-05T08:43:26.151313+00:00","updated_at":"2026-07-05T08:43:26.151313+00:00"}