{"paper":{"title":"Augmented Language Models: a Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Augmented language models combine reasoning and tool use to address traditional LM limits on interpretability, consistency, and scalability.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Asli Celikyilmaz, Baptiste Rozi\\`ere, Christoforos Nalmpantis, Edouard Grave, Gr\\'egoire Mialon, Jane Dwivedi-Yu, Maria Lomeli, Ram Pasunuru, Roberta Raileanu, Roberto Dess\\`i, Thomas Scialom, Timo Schick, Yann LeCun","submitted_at":"2023-02-15T18:25:52Z","abstract_excerpt":"This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics, or learn to do so from demonstrations. While adhering to a standard missing tokens prediction objective, such augmented LMs can use various, possibly non-parametric external modules to expand their context processing ability, t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the reviewed augmentations with reasoning and tools will demonstrably improve interpretability, consistency, and scalability in practice, as implied by the synthesis of existing works.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Augmented language models combine reasoning and tool-calling abilities with standard language modeling to improve performance on complex tasks while retaining core capabilities.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Augmented language models combine reasoning and tool use to address traditional LM limits on interpretability, consistency, and scalability.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"51627ebcc7a258dd0d199c6ed0bea05ab5cc8c3f033bbceb8922699df2a7bcaf"},"source":{"id":"2302.07842","kind":"arxiv","version":1},"verdict":{"id":"e74b1d60-cce0-496a-ac4e-4e6762a436e1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T02:35:15.768907Z","strongest_claim":"this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.","one_line_summary":"Augmented language models combine reasoning and tool-calling abilities with standard language modeling to improve performance on complex tasks while retaining core capabilities.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the reviewed augmentations with reasoning and tools will demonstrably improve interpretability, consistency, and scalability in practice, as implied by the synthesis of existing works.","pith_extraction_headline":"Augmented language models combine reasoning and tool use to address traditional LM limits on interpretability, consistency, and scalability."},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"3ca75d5136476388d0cc6c64103e060fe92a2e4f3fea41ecf8318ee7475f3683"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}