Language agents perform complex tasks by using tools to execute each step
precisely. However, most existing agents are based on proprietary models or
designed to target specific tasks, such as mathematics or multi-hop question
answering. We introduce Husky, a holistic, open-source language agent that
learns to reason over a unified action space to address a diverse set of
complex tasks involving numerical, tabular, and knowledge-based reasoning.
Husky iterates between two stages: 1) generating the next action to take
towards solving a given task and 2) executing the action using expert models
and updating the current solution state. We identify a thorough ontology of
actions for addressing complex tasks and curate high-quality data to train
expert models for executing these actions. Our experiments show that Husky
outperforms prior language agents across 14 evaluation datasets. Moreover, we
introduce HuskyQA, a new evaluation set which stress tests language agents for
mixed-tool reasoning, with a focus on retrieving missing knowledge and
performing numerical reasoning. Despite using 7B models, Husky matches or even
exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of
our holistic approach in addressing complex reasoning problems. Our code and
models are available at this https URL

Husky 是一个开放源代码的语言代理（language agent），通过在统一的行动空间上进行推理来解决涉及数值、表格和基于知识的复杂任务，它的实验结果显示在 14 个评估数据集上优于先前的语言代理，并且在混合工具推理方面甚至能与 GPT-4 等高级语言模型媲美。

Husky 多步推理的统一开源语言代理

Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning

Diffusion-based image generation models such as DALL-E 3 and Stable
Diffusion-XL demonstrate remarkable capabilities in generating images with
realistic and unique compositions. Yet, these models are not robust in
precisely reasoning about physical and spatial configurations of objects,
especially when instructed with unconventional, thereby out-of-distribution
descriptions, such as "a chair with five legs". In this paper, we propose a
language agent with chain-of-3D-thoughts (L3GO), an inference-time approach
that can reason about part-based 3D mesh generation of unconventional objects
that current data-driven diffusion models struggle with. More concretely, we
use large language models as agents to compose a desired object via
trial-and-error within the 3D simulation environment. To facilitate our
investigation, we develop a new benchmark, Unconventionally Feasible Objects
(UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender
where language agents can build and compose atomic building blocks via API
calls. Human and automatic GPT-4V evaluations show that our approach surpasses
the standard GPT-4 and other language agents (e.g., ReAct and Reflexion) for 3D
mesh generation on ShapeNet. Moreover, when tested on our UFO benchmark, our
approach outperforms other state-of-the-art text-to-2D image and text-to-3D
models based on human evaluation.

通过 L3GO 模型，利用大型语言模型作为代理，使用 3D 仿真环境进行试错式组合，推理和生成 Diffusion-based 图像模型中难以处理的非传统对象的基于部件的 3D 网格生成。在 ShapeNet 和 UFO 基准上，我们的方法在人工和自动评估中表现优于标准 GPT-4 和其他语言代理。