This paper aims to efficiently enable Large Language Models (LLMs) to use multi-modal tools. Advanced proprietary LLMs, such as ChatGPT and GPT -4, have shown great potential for tool usage through sophisticated prompt engineering.

Scientists are rethinking what cattle are capable of after an Austrian cow named Veronika was found to use tools with impressive skill. The discovery, reported by researchers in Vienna, suggests cows may have far greater cognitive abilities than previously assumed. Veronika, a cow living in a mountain village in the Austrian countryside, has spent years perfecting the art of scratching herself using sticks, rakes, and brooms. Word of her behaviour eventually reached animal intelligence specialists in Vienna, who found Veronika used both ends of the same object for different tasks. If it were her back or another tough area that warranted a good scratch, she would use the bristle end of a broom.

Large Language Models (LLMs) have seen an impressive wave of advances, withmodels now excelling in a variety of tasks, such as mathematical reasoning andprogram synthesis. However, their potential to effectively use tools via API callsremains unfulfilled. This is a challenging task even for today's state-of-the-artLLMs such as GPT-4 largely due to their unawareness of what APIs are availableand how to use them in a frequently updated tool set. We develop Gorilla, afinetuned LLaMA model that surpasses the performance of GPT-4 on writing APIcalls. Trained with the novel Retriever Aware Training (RAT), when combinedwith a document retriever, Gorilla demonstrates a strong capability to adapt totest-time document changes, allowing flexible user updates or version changes.It also substantially mitigates the issue of hallucination, commonly encounteredwhen prompting LLMs directly. To evaluate the model's ability, we introduceAPIBench, a comprehensive dataset consisting of HuggingFace, TorchHub, andTensorHub APIs. The successful integration of the retrieval system with Gorillademonstrates the potential for LLMs to use tools more accurately, keep up withfrequently updated documentation, and consequently increase the reliability andapplicability of their outputs. Gorilla's code, model, data, and demo are availableat: https://gorilla.cs.berkeley.edu

This paper aims to efficiently enable Large Language Models (LLMs) to use multi-modal tools.The advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering.Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data.To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools.It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts.By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation.Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways.Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools.

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. In this paper, we show that LMs can teach themselves to via simple APIs and achieve the best of both worlds. We introduce, 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. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.

The gray wolf reeled in a crab trap with a rope, sparking a debate among biologists. Breakthroughs, discoveries, and DIY tips sent every weekday. Some 300 miles north of Vancouver, nestled among the rocky bays and forests of the Haíɫzaqv Nation, a wily gray wolf helps itself to a snack. On its own, this isn't remarkable and happens all the time. But a wild wolf swimming to a buoy, reeling it in, and then pulling an underwater trap to shore before eating the bait?

Work done during an internship at Tencent.

This paper aims to efficiently enable Large Language Models (LLMs) to use multi-modal tools. Advanced proprietary LLMs, such as ChatGPT and GPT -4, have shown great potential for tool usage through sophisticated prompt engineering.

Large Language Models (LLMs) have seen an impressive wave of advances, withmodels now excelling in a variety of tasks, such as mathematical reasoning andprogram synthesis. However, their potential to effectively use tools via API callsremains unfulfilled. This is a challenging task even for today's state-of-the-artLLMs such as GPT-4 largely due to their unawareness of what APIs are availableand how to use them in a frequently updated tool set. We develop Gorilla, afinetuned LLaMA model that surpasses the performance of GPT-4 on writing APIcalls. Trained with the novel Retriever Aware Training (RAT), when combinedwith a document retriever, Gorilla demonstrates a strong capability to adapt totest-time document changes, allowing flexible user updates or version changes.It also substantially mitigates the issue of hallucination, commonly encounteredwhen prompting LLMs directly.