Summary of Language Hooks: a Modular Framework For Augmenting Llm Reasoning That Decouples Tool Usage From the Model and Its Prompt, by Damien De Mijolla et al.
Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
by Damien de Mijolla, Wen Yang, Philippa Duckett, Christopher Frye, Mark Worrall
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called language hooks for augmenting language models with new capabilities. The approach is decoupled from the model’s task-specific prompt and from the model itself. Language hooks interleave text generation by the base model with the execution of modular programs that trigger conditionally based on the existing text and available capabilities. This allows programs to call external tools, auxiliary language models, or modify the existing context. The paper benchmarks its method against state-of-the-art baselines and finds it outperforms task-aware approaches. The framework is demonstrated to generalize to novel tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language hooks are a new way to make language models more powerful. They let you add new abilities to a model without needing special prompts or tying the ability to a specific task. This makes it easier to use tools and other capabilities with language models. The authors tested their idea and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Prompt » Text generation
