ToolGen: Unified Tool Retrieval and Calling via Generation

As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often inefficient, retrieval mechanisms. We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the LLM's parameters by representing each tool as a unique token. This enables the LLM to generate tool calls and arguments as part of its next token prediction capabilities, seamlessly blending tool invocation with language generation. Our framework allows the LLM to access and utilize a vast amount of tools with no additional retrieval step, significantly enhancing both performance and scalability. Experimental results with over 47,000 tools show that ToolGen not only achieves superior results in both tool retrieval and autonomous task completion but also sets the stage for a new era of AI agents that can adapt to tools across diverse domains. By fundamentally transforming tool retrieval into a generative process, ToolGen paves the way for more versatile, efficient, and autonomous AI systems. ToolGen enables end-to-end tool learning and opens opportunities for integration with other advanced techniques such as chain-of-thought and reinforcement learning, thereby expanding the practical capabilities of LLMs.

Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed. Faced with the problem, the field of red teaming is experiencing fast-paced growth, which highlights the need for a comprehensive organization covering the entire pipeline and addressing emerging topics for the community. Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models. Additionally, we have developed the searcher framework that unifies various automatic red teaming approaches. Moreover, our survey covers novel areas including multimodal attacks and defenses, risks around multilingual models, overkill of harmless queries, and safety of downstream applications. We hope this survey can provide a systematic perspective on the field and unlock new areas of research.

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools like search engines. However, LLMs are not optimized specifically for tool use during training or alignment, limiting their effectiveness as agents. To resolve this problem, previous work has collected interaction trajectories between GPT-4 and environments, and fine-tuned smaller models with them. As part of this, the standard approach has been to simply discard trajectories that do not finish the task successfully, which, on the one hand, leads to a significant waste of data and resources, and on the other hand, has the potential to limit the possible optimization paths during fine-tuning. In this paper, we contend that large language models can learn from failures through appropriate data cleaning and fine-tuning strategies. We conduct experiments on mathematical reasoning, multi-hop question answering, and strategic question answering tasks. Experimental results demonstrate that compared to solely using positive examples, incorporating negative examples enhances model performance by a large margin.

Understanding the Instruction Mixture for Large Language Model Fine-tuning

While instructions fine-tuning of large language models (LLMs) has been proven to enhance performance across various applications, the influence of the instruction dataset mixture on LLMs has not been thoroughly explored. In this study, we classify instructions into three main types: NLP downstream tasks, coding, and general chatting, and investigate their impact on LLMs. Our findings reveal that specific types of instructions are more beneficial for particular uses, while it may cause harms to other aspects, emphasizing the importance of meticulously designing the instruction mixture to maximize model performance. This study sheds light on the instruction mixture and paves the way for future research.