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

Toxic language is difficult to define, as it is not monolithic and has many
variations in perceptions of toxicity. This challenge of detecting toxic
language is increased by the highly contextual and subjectivity of its
interpretation, which can degrade the reliability of datasets and negatively
affect detection model performance. To fill this void, this paper introduces a
toxicity inspector framework that incorporates a human-in-the-loop pipeline
with the aim of enhancing the reliability of toxicity benchmark datasets by
centering the evaluator's values through an iterative feedback cycle. The
centerpiece of this framework is the iterative feedback process, which is
guided by two metric types (hard and soft) that provide evaluators and dataset
creators with insightful examination to balance the tradeoff between
performance gains and toxicity avoidance.

本文介绍了一种毒性语言检测框架，通过考虑人为因素通过迭代反馈循环来提高毒性基准数据集的可靠性，以平衡性能和毒性避免之间的权衡。

毒性检测评估框架：通过反馈评估毒性检测中的基本事实

Toxicity Inspector: A Framework to Evaluate Ground Truth in Toxicity  Detection Through Feedback

Like people, LLMs do not always generate the best text for a given generation
problem on their first try (e.g., summaries, answers, explanations). Just as
people then refine their text, we introduce SELF-REFINE, a framework for
similarly improving initial outputs from LLMs through iterative feedback and
refinement. The main idea is to generate an output using an LLM, then allow the
same model to provide multi-aspect feedback for its own output; finally, the
same model refines its previously generated output given its own feedback.
Unlike earlier work, our iterative refinement framework does not require
supervised training data or reinforcement learning, and works with a single
LLM. We experiment with 7 diverse tasks, ranging from review rewriting to math
reasoning, demonstrating that our approach outperforms direct generation. In
all tasks, outputs generated with SELF-REFINE are preferred by humans and by
automated metrics over those generated directly with GPT-3.5 and GPT-4,
improving on average by absolute 20% across tasks.

通过引入 SELF-REFINE 框架，可以通过迭代反馈和改进从 LLMs 获得更好的输出，同时不需要监督训练数据或强化学习，且在 7 种任务中展现出优越性能。