Tool learning aims to enhance and expand large language models' (LLMs)
capabilities with external tools, which has gained significant attention
recently. Current methods have shown that LLMs can effectively handle a certain
amount of tools through in-context learning or fine-tuning. However, in
real-world scenarios, the number of tools is typically extensive and
irregularly updated, emphasizing the necessity for a dedicated tool retrieval
component. Tool retrieval is nontrivial due to the following challenges: 1)
complex user instructions and tool descriptions; 2) misalignment between tool
retrieval and tool usage models. To address the above issues, we propose to
enhance tool retrieval with iterative feedback from the large language model.
Specifically, we prompt the tool usage model, i.e., the LLM, to provide
feedback for the tool retriever model in multi-round, which could progressively
improve the tool retriever's understanding of instructions and tools and reduce
the gap between the two standalone components. We build a unified and
comprehensive benchmark to evaluate tool retrieval models. The extensive
experiments indicate that our proposed approach achieves advanced performance
in both in-domain evaluation and out-of-domain evaluation.

通过迭代反馈的方式，提高大型语言模型对工具检索的理解，从而在领域内外的评估中取得卓越表现。

利用大型语言模型的迭代反馈增强工具检索

Enhancing Tool Retrieval with Iterative Feedback from Large Language  Models

Large language models (LLMs) are increasingly employed for complex multi-step
planning tasks, where the tool retrieval (TR) step is crucial for achieving
successful outcomes. Two prevalent approaches for TR are single-step retrieval,
which utilizes the complete query, and sequential retrieval using task
decomposition (TD), where a full query is segmented into discrete atomic
subtasks. While single-step retrieval lacks the flexibility to handle
"inter-tool dependency," the TD approach necessitates maintaining "subtask-tool
atomicity alignment," as the toolbox can evolve dynamically. To address these
limitations, we introduce the Progressive Tool retrieval to Improve Planning
(ProTIP) framework. ProTIP is a lightweight, contrastive learning-based
framework that implicitly performs TD without the explicit requirement of
subtask labels, while simultaneously maintaining subtask-tool atomicity. On the
ToolBench dataset, ProTIP outperforms the ChatGPT task decomposition-based
approach by a remarkable margin, achieving a 24% improvement in Recall@K=10 for
TR and a 41% enhancement in tool accuracy for plan generation.

ProTIP 是一种轻量级、基于对比学习的框架，可以在不需要明确的子任务标签的情况下隐式地进行任务分解，同时保持子任务与工具的原子性。在 ToolBench 数据集上，ProTIP 相比于基于 ChatGPT 的任务分解方法，在工具检索和计划生成方面取得了显著的性能提升，检索召回率提高了 24%，工具准确性提高了 41%。

ProTIP：渐进式工具检索改进规划

ProTIP: Progressive Tool Retrieval Improves Planning

Large language models (LLMs) have the remarkable ability to solve new tasks
with just a few examples, but they need access to the right tools. Retrieval
Augmented Generation (RAG) addresses this problem by retrieving a list of
relevant tools for a given task. However, RAG's tool retrieval step requires
all the required information to be explicitly present in the query. This is a
limitation, as semantic search, the widely adopted tool retrieval method, can
fail when the query is incomplete or lacks context. To address this limitation,
we propose Context Tuning for RAG, which employs a smart context retrieval
system to fetch relevant information that improves both tool retrieval and plan
generation. Our lightweight context retrieval model uses numerical,
categorical, and habitual usage signals to retrieve and rank context items. Our
empirical results demonstrate that context tuning significantly enhances
semantic search, achieving a 3.5-fold and 1.5-fold improvement in Recall@K for
context retrieval and tool retrieval tasks respectively, and resulting in an
11.6% increase in LLM-based planner accuracy. Additionally, we show that our
proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART
outperforms GPT-4 based retrieval. Moreover, we observe context augmentation at
plan generation, even after tool retrieval, reduces hallucination.

利用上下文调整的检索增强生成模型可以通过智能上下文检索系统获取相关信息，从而提高工具检索和计划生成的准确性。其中，语义搜索在不完整或缺乏上下文的情况下容易失效。经验证明，上下文调整显著提高了语义搜索的性能，并且对于上下文检索和工具检索任务分别实现了 3.5 倍和 1.5 倍的提升，同时使基于 LLM 的计划生成的准确性增加了 11.6％。此外，在使用倒数排名融合（RRF）和 LambdaMART 的轻量级模型时，还观察到了计划生成阶段的上下文增强可以减少虚构现象。