Embodied environments present a challenge that calls for logical reasoning to solve complex sequential decision-making tasks. To overcome these obstacles, a common strategy is to employ reinforcement learning (RL), in which agents learn from their experiences in the real world. Despite significant developments in deep RL techniques, it is still difficult to address the complexities of these difficult tasks efficiently and securely.
Meanwhile, recent advances in large language models (LLMs) provide some hope. Notable works such as Radford et al. (2019), ChatGPT 3 [Brown et al.2020], and Wei et al. (2022) have shown that LLMs have the capacity for reasoning. These researches demonstrate that LLMs are capable of reasoning activities. This makes them possible contenders for solving the difficulties of embodied sequential decision-making problems.
While research has been conducted on incorporating pre-trained LLMs into embodied agents, actual implementation still needs to be improved. Taking action based on LLM predictions can be risky because of the inherent uncertainty in real-world problems.
What is LLM agent?
An LLM agent is an AI system that goes beyond simple text production. It uses a large language model (LLM) as its central computational engine, allowing it to carry on conversations, do tasks, reason, and display a degree of autonomy.
Carefully crafted prompts that encode identities, instructions, authorization, and context guide LLM agents and shape their replies and activities.
Components of Agents
1. Core LLM
The LLM agent's foundation is the large language model upon which it is built. This neural network, trained using massive datasets, can generate and understand simple texts. The LLM's size and design set the agent's initial capabilities and constraints.
Prompt recipes that activate and direct the LLM's abilities are also crucial. The agent's identity, expertise, behaviours, and objectives are all imparted to it using carefully prepared prompts. Prompt recipes provide pre-defined templates that combine important instructions, contexts, and parameters to elicit the necessary agent answers.
Conversational agents need personas encoded in prompts to adopt different voices. Prompts help task-oriented agents by clarifying goals, supplying pertinent information, and framing instructions.
Agents' memory is crucial because it provides a temporal framework and stores fine-grained details relevant to specific users or tasks. There are two main types of memory that agents use to improve their performance:
- The first is our innate capacity for short-term memory, which is central to the Large Language Model (LLM) and ensures that we never lose track of ongoing conversations or our most recent acts. This agent's short-term memory serves as a shifting context window, helping it make sense in current conversations.
- The second type of memory is long-term memory, where an external database is combined with the LLM to greatly increase the LLM's ability to remember voluminous amounts of data. This enhancement broadens the agent's memory to include information, conversations, and other things from a longer period. The agent can tap into accumulated knowledge and insights by including this form of long-term memory.
When put together, these pieces of information give the agent a firm grasp of the past and contextual knowledge about the current user. This contextual groundwork adds a human touch to discussions by considering previous interactions and improves the agent's reliability and skill while carrying out complex procedures. Simply put, the agent's memory allows them to make conversations more personal and interesting for both parties, leading to deeper connections and better results in their work.
In LLM agents, two distinct aspects play a vital role in shaping their capabilities: memory and knowledge. While memory is concerned with retaining user interactions and specific task-related details over a temporal framework, knowledge represents broader expertise that can be applied across different users and tasks. Knowledge enhances and broadens the foundation contained within the model's inherent parameters.
- Specialized knowledge is a crucial facet that supplements the core architecture of AI. It introduces domain-specific terminologies, concepts, and modes of reasoning that are finely tuned to particular topics or fields. This augmentation allows the AI to engage more deeply and accurately in discussions related to these specialized areas, making it a more valuable resource for users seeking expertise in those domains.
- Commonsense knowledge is another dimension that enriches the AI's capabilities. It imparts a general understanding of the world that the model might lack by introducing facts and insights about society, culture, science, and various other domains. This layer of knowledge helps the AI generate responses that align with human common sense, making interactions more relatable and realistic.
- Procedural knowledge equips the AI with the practical skills and methods required to accomplish specific tasks. Whether understanding complex workflows, employing analytical techniques, or engaging in creative processes, procedural knowledge empowers the AI to provide practical guidance and solutions.
Integrating knowledge into the AI's architecture expands its capacity to comprehend and engage in meaningful discussions. Knowledge remains pertinent even as the AI's memory is reset or adapted for different tasks. This harmonious combination results in AI agents that possess a reservoir of personalized memories and a wealth of pertinent expertise. This amalgamation of memory and knowledge transforms AI into knowledgeable, conversational partners capable of catering to diverse user needs.
5. Tool Integration
Integration of tools allows agents to carry out their duties using other services and APIs rather than relying entirely on language generation. For instance, a code interpreter like OpenAi's could be used by an agent as a code execution tool to carry out software operations mentioned in a prompt.
Agents architectures and different Agent types
1. Web agents
In recent advancements within AI research, two pivotal dimensions have emerged - the Web Agent and the Tool Agent. The Web Agent trains AI models for effective web navigation, a foundational skill for information gathering and communication. Previous endeavors have explored web agent training in simulation environments, and more recently, the integration of Language-Action Agents (LAA) has brought forth innovative solutions.
Noteworthy examples include MIND2Web, which refines language models for generating executable web actions, and WebAgent, which decomposes task instructions into sub-tasks to produce executable Python programs for web navigation directly. Realistic task simulation, as seen in WebArena, and seamless web integration plugins like Langchain and ChatGPT indicate the growing importance of web navigation as a fundamental task where LAA's capabilities truly shine.
2. Tool agents
The concept of a Tool Agent has gained prominence, exemplifying how AI models harness external tools to amplify their problem-solving capabilities. Pioneering initiatives like "Gorilla" master the art of generating API calls and adapting to document changes in real time. The "ToolLLM" framework has emerged as an open-source solution for effective interactions with various tools, particularly APIs, for intricate task execution. This framework includes ToolBench, a dataset designed to fine-tune instructions for tool utilization.
A recent shift in teaching AI models to employ new tools has been highlighted, advocating the utilization of tool documentation. Empirical evidence from this perspective suggests that relying solely on detailed tool documentation in the form of zero-shot prompts can rival the performance of prompts designed with a few-shot learning approach. These advancements underscore the evolving landscape where AI collaborates with tools to accomplish complex tasks innovatively and efficiently.
LLM Agents Application
An open-source Python program, Auto GPT, was developed from the ground up on the same framework as GPT-4. It was recently published on GitHub and was created by Toran Bruce Richards. The core concept is the hands-off performance of routine tasks without user intervention. After receiving a goal prompt from the user, Auto GPT will carry out the necessary procedures to accomplish that goal. The Auto GPT framework is based on so-called 'AI agents,' which use online access to do activities without human intervention.
Auto GPT and Chat GPT are both built on the GPT-4 foundation. YetHowever, we know very little about how to tell the two languages apart.
AutoGPT vs ChatGPT
- Chat GPT gives information and responds to individual inquiries, whereas Auto GPT automates the execution of a whole job depending on the instructions it receives.
- Larger tasks may be carried out by Auto GPT thanks to its availability of data from the internet, social media, processed data, market trends, and customer behavior, all of which can inform its decisions. However, Chat GPT would need detailed instructions on each step to provide the necessary data.
- Auto GPT offers more independence than Chat GPT, which can only respond to questions using the data it was trained on.
Taking cues from how humans learn, BabyAGI is an open-source AI platform designed to train and assess different AI agents in a simulated setting. The platform is geared towards helping AI agents learn and carry out difficult tasks using a combination of reinforcement learning, language acquisition, and cognitive growth.
BabyAGI uses robust technologies like GPT-4, the chain and agent capabilities of LangChain, the OpenAI API, and Pinecone. With an emphasis on reinforcement learning and language, these technologies facilitate effective task completion, development, prioritization, and storage.
- Execution agent
- Task creation agent
- Prioritization agent
The task generation agent uses OpenAI's API calls to break down a human-defined goal into a series of tasks. Then, the prioritization agent does what it's supposed to: ranks and stores the tasks in order of importance. In the end, the execution agent completes the tasks it can and often reveals and queues up upcoming activities. The cycle begins again when the priorities have been rearranged. This multi-agent process is depicted in the following image.
The US Census Database may be easily accessed and explored with the help of Census GPT, an AI-powered search engine. Natural language processing is used to decipher user queries, making for a quick and easy search experience.
Ask a query, and CensusGPT will respond in tabular data and visualizations, resolving the issue of inaccessible census data. Researchers, economists, and anyone interested in using freely available census data to address demographics, ethnicity, and wealth issues will find this tool invaluable. CensusGPT is based on the public-domain TextSQL project, which uses artificial intelligence to transform queries into SQL so that users may "talk" to any dataset in their native language.
How to implement LLM Agents
1. Data Collection
Compile a sizable and varied corpus of text that is pertinent to the area you want the LLM agent to focus on. The language model will be trained using this dataset.
2. Preprocessing Data
Clean up and preprocess the text data gathered by removing noise, inconsistent formatting, and superfluous information. Tokenize the text to break it up into more manageable chunks for model training.
3. Training the Language Model
Use ML methods, particularly NLP strategies, to train the language model using the preprocessed dataset. Transformer models and other deep learning architectures are useful for training LLM agents. During training, text sequences are fed to the language model while its parameters are optimized to learn the statistical relationships and patterns found in the data.
To improve performance and adapt the pre-trained language model to your intended use case, fine-tune it to a more specific task or area. To achieve this, the model must be trained on a dataset unique to the job while retaining prior knowledge.
5. Evaluation and Iteration
Assess the LLM agent's performance using the proper metrics, such as perplexity or accuracy, and make necessary model revisions. Improve the agent's abilities over time by iterating the training and fine-tuning procedure.
6. Deployment and Integration
Deploy the LLM agent in a production environment or integrate it into the platform or application you want once its performance is satisfactory. The APIs or interfaces required for communication with the agent.
7. Continuous Learning and Improvement
Regularly update and retrain the LLM agent with the most recent knowledge. By doing this, the agent is kept current and keeps being relevant over time.
Challenges of LLM agents
1. UX issues
The current agent system uses natural language to connect LLMs with other components like memory and tools. However, LLMs can make formatting errors and occasionally exhibit rebellious behaviour (e.g., refuse to follow an order), so it's hard to trust the results of these models. Because of this, much of the agent demonstration code is dedicated to analysing model output.
2. long-term planning and task decomposition
Long-term planning and thorough solution space exploration remain formidable obstacles. LLMs are not as resilient as opposed to people who learn through trial and error because they are unable to change plans in the face of unforeseen errors.
3. Trust and privacy
Users have no say over when the tool is executed (LLM does it) or how the utility is invoked. Is it risky to utilise any old third-party plugin or tool? This is a major issue if the programme can access sensitive information or perform "admin" level tasks (like sending emails) independently. How can we ensure that 3P plugins and tools don't behave maliciously or introduce new tokens into the system?
4. Tool Quaility
Context length is finite, therefore, there are restrictions on how much background data, specific directions, API call context, and subsequent responses can be included. While processes like self-reflection to learn from past mistakes would greatly benefit from extended or infinite context windows, the system's design must work with the available communication capacity. Access to a broader body of knowledge is possible through vector stores and retrieval. However, this does not mean they are as effective at representing that body of knowledge as undivided focus.
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