MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

Wenhao Yu Hongming Zhang Wenlin Yao + 6 lainnya

12 September 2024

Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed to investigate their performance in the data science domain. However, existing data science benchmarks still fall short when compared to real-world data science applications due to their simplified settings. To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. DSBench offers a realistic setting by encompassing long contexts, multimodal task backgrounds, reasoning with large data files and multi-table structures, and performing end-to-end data modeling tasks. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG). These findings underscore the need for further advancements in developing more practical, intelligent, and autonomous data science agents.

B. Labash Federico Cassano Shunyu Yao + 3 lainnya

20 Maret 2023

Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning. We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback. Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials. Reflexion is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals, and obtains significant improvements over a baseline agent across diverse tasks (sequential decision-making, coding, language reasoning). For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%. We also conduct ablation and analysis studies using different feedback signals, feedback incorporation methods, and agent types, and provide insights into how they affect performance.

P. Lincoln Nathaniel D. Bastian S. Neema + 3 lainnya

1 Juni 2023

Large language models (LLMs) such as ChatGPT have been trained to generate human-like responses to natural language prompts. LLMs use a vast corpus of text data for training, and can generate coherent and contextually relevant responses to a wide range of questions and statements. Despite this remarkable progress, LLMs are prone to hallucinations making their application to safety-critical applications such as autonomous systems difficult. The hallucinations in LLMs refer to instances where the model generates responses that are not factually accurate or contextually appropriate. These hallucinations can occur due to a variety of factors, such as the model’s lack of real-world knowledge, the influence of biased or inaccurate training data, or the model’s tendency to generate responses based on statistical patterns rather than a true understanding of the input. While these hallucinations are a nuisance in tasks such as text summarization and question-answering, they can be catastrophic when LLMs are used in autonomy-relevant applications such as planning. In this paper, we focus on the application of LLMs in autonomous systems and sketch a novel self-monitoring and iterative prompting architecture that uses formal methods to detect these errors in the LLM response automatically. We exploit the dialog capability of LLMs to iteratively steer them to responses that are consistent with our correctness specification. We report preliminary experiments that show the promise of the proposed approach on tasks such as automated planning.

L. Langosco Tomasz Korbak Rachel Freedman + 29 lainnya

27 Juli 2023

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.

S. Lamprier Clément Romac Olivier Sigaud + 3 lainnya

6 Februari 2023

Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment between LLMs' knowledge and the environment can be wrong and limit functional competence due to lack of grounding. In this paper, we study an approach (named GLAM) to achieve this alignment through functional grounding: we consider an agent using an LLM as a policy that is progressively updated as the agent interacts with the environment, leveraging online Reinforcement Learning to improve its performance to solve goals. Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks? 2) How can it boost different forms of generalization? 3) What is the impact of online learning? We study these questions by functionally grounding several variants (size, architecture) of FLAN-T5.