Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt

• URL: http://arxiv.org/abs/2412.05967v1
• Date: Sun, 08 Dec 2024 15:16:17 GMT
• Title: Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
• Authors: Damien de Mijolla, Wen Yang, Philippa Duckett, Christopher Frye, Mark Worrall,
• Abstract summary: We introduce language hooks, a novel framework for augmenting language models with new capabilities. We benchmark our method against state-of-the-art baselines, find that it outperforms task-aware approaches.
• Score: 7.096646842716599
• License:
• Abstract: Prompting and fine-tuning have emerged as two competing paradigms for augmenting language models with new capabilities, such as the use of tools. Prompting approaches are quick to set up but rely on providing explicit demonstrations of each tool's usage in the model's prompt, thus coupling tool use to the task at hand and limiting generalisation. Fine-tuning removes the need for task-specific demonstrations of tool usage at runtime; however, this ties new capabilities to a single model, thus making already-heavier setup costs a recurring expense. In this paper, we introduce language hooks, a novel framework for augmenting language models with new capabilities that is decoupled both from the model's task-specific prompt and from the model itself. The language hook algorithm interleaves text generation by the base model with the execution of modular programs that trigger conditionally based on the existing text and the available capabilities. Upon triggering, programs may call external tools, auxiliary language models (e.g. using tool specific prompts), and modify the existing context. We benchmark our method against state-of-the-art baselines, find that it outperforms task-aware approaches, and demonstrate its ability to generalise to novel tasks.

Related papers

• FREYR: A Framework for Recognizing and Executing Your Requests [2.4797200957733576]
  This paper introduces FREYR, a streamlined framework that modularizes the tool usage process into separate steps. We show that FREYR achieves superior performance compared to conventional tool usage methods. We evaluate FREYR on a set of real-world test cases specific for video game design and compare it against traditional tool usage as provided by the Ollama API.
  arXiv  Detail & Related papers  (2025-01-21T11:08:18Z)
• CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
  This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
  arXiv  Detail & Related papers  (2024-04-03T02:21:46Z)
• ART: Automatic multi-step reasoning and tool-use for large language models [105.57550426609396]
  Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings. Each reasoning step can rely on external tools to support computation beyond the core LLM capabilities. We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program.
  arXiv  Detail & Related papers  (2023-03-16T01:04:45Z)
• Augmented Language Models: a Survey [55.965967655575454]
  This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. We refer to them as Augmented Language Models (ALMs) The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks.
  arXiv  Detail & Related papers  (2023-02-15T18:25:52Z)
• Toolformer: Language Models Can Teach Themselves to Use Tools [62.04867424598204]
  Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. We show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction.
  arXiv  Detail & Related papers  (2023-02-09T16:49:57Z)
• Prompting Is Programming: A Query Language for Large Language Models [5.8010446129208155]
  We present the novel idea of Language Model Programming (LMP) LMP generalizes language model prompting from pure text prompts to an intuitive combination of text prompting and scripting. We show that LMQL can capture a wide range of state-of-the-art prompting methods in an intuitive way.
  arXiv  Detail & Related papers  (2022-12-12T18:09:09Z)
• Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models [116.25562358482962]
  State-of-the-art neural language models can be used to solve ad-hoc language tasks without the need for supervised training. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts.
  arXiv  Detail & Related papers  (2022-08-16T17:17:53Z)
• Few-shot Prompting Towards Controllable Response Generation [49.479958672988566]
  We first explored the combination of prompting and reinforcement learning (RL) to steer models' generation without accessing any of the models' parameters. We apply multi-task learning to make the model learn to generalize to new tasks better. Experiment results show that our proposed method can successfully control several state-of-the-art (SOTA) dialogue models without accessing their parameters.
  arXiv  Detail & Related papers  (2022-06-08T14:48:06Z)
• EXPATS: A Toolkit for Explainable Automated Text Scoring [2.299617836036273]
  We present EXPATS, an open-source framework to allow users to develop and experiment with different ATS models quickly. The toolkit also provides seamless integration with the Language Interpretability Tool (LIT) so that one can interpret and visualize models and their predictions.
  arXiv  Detail & Related papers  (2021-04-07T19:29:06Z)
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm [0.0]
  We discuss methods of prompt programming, emphasizing the usefulness of considering prompts through the lens of natural language. We introduce the idea of a metaprompt that seeds the model to generate its own natural language prompts for a range of tasks.
  arXiv  Detail & Related papers  (2021-02-15T05:27:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.