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Augmented Language Models: a Survey

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2302.07842

Summary

Large language models still hallucinate, slip on arithmetic and multi-step inference, and need huge parameter counts to memorise knowledge. The authors attribute this to their reliance on one parametric network and a limited context window. This Meta AI survey (one co-author is at Universitat Pompeu Fabra) maps research on Augmented Language Models (ALMs). These models keep the standard missing-token prediction objective but enrich their context in two ways. One is reasoning, meaning splitting a hard problem into easier subtasks. The other is tools: external modules such as retrievers, search engines, code interpreters or robotic arms, whose outputs are fed back into the context. The paper defines reasoning, tool and act. It then sorts the literature along three axes: how reasoning is elicited, which tools and actions are used, and whether these behaviours are hand-designed heuristics or learned. It proposes no new model or dataset. The authors argue ALMs could reduce interpretability, consistency and scaling problems. However, most methods still depend on human annotation, and few combine reasoning with tools.

Key points

  • Structure of the taxonomy:
    • Reasoning: prompting-based elicitation, recursive prompting, and explicit training to reason.
    • Tools and acting: calling other models, information retrieval (retrieval-augmented LMs, querying search engines, web navigation), symbolic modules and code interpreters, and acting in virtual or physical environments.
    • Learning: supervision versus reinforcement learning.
  • Retrieval-augmented models: systems such as Borgeaud et al. (2022) and Izacard et al. (2022) can match some much larger LMs with fewer parameters.
  • Compositionality gap (Press et al., 2022): a model may know a person's birth and death dates yet still compute their age incorrectly.
  • Claimed benefits and caveats: the claimed benefits are truthfulness, uncertainty estimation, interpretability and broader capabilities. There are caveats, though:
    • Evidence that retrieval improves factuality is scarce (Krishna et al., 2021).
    • Chain-of-thought can produce correct answers from meaningless intermediate steps (Lewkowycz et al., 2022).
  • Ethics and open problems:
    • The authors separate passive tools (which only gather information) from active ones (which act without human validation) and flag the latter as riskier.
    • Fully self-supervised augmentation remains an open question.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

228 papers of our library cite it (of the 140673 whose bibliographies we read); 22 of them have a page here, 206 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2302.07842 · arXiv 2302.07842

@misc{mialon2023augmented,
  title = {{Augmented Language Models: a Survey}},
  author = {Mialon, Grégoire and Dessì, Roberto and Lomeli, Maria and Nalmpantis, Christoforos and Pasunuru, Ram and Raileanu, Roberta and Rozière, Baptiste and Schick, Timo and Dwivedi-Yu, Jane and Celikyilmaz, Asli and Grave, Edouard and LeCun, Yann and Scialom, Thomas},
  year = {2023},
  doi = {10.48550/arxiv.2302.07842},
  eprint = {2302.07842},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2302.07842}
}

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