Agents: An Open-source Framework for Autonomous Language Agents

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.

A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data

This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset.

Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks

Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either improving the network architecture or adding adversarial examples into the training set, with their respective limitations. We propose a new direction. Motivated by recent research that shows that outliers in the training set have a high negative influence on the trained model, our approach makes the model more robust by detecting and removing outliers in the training set without modifying the network architecture or requiring adversarial examples. We propose two methods for detecting outliers based on canonical examples and on training errors, respectively. After removing the outliers, we train the classifier with the remaining examples to obtain a sanitized model. Our evaluation shows that the sanitized model improves classification accuracy and forces the attacks to generate adversarial examples with higher distortions. Moreover, the Kullback-Leibler divergence from the output of the original model to that of the sanitized model allows us to distinguish between normal and adversarial examples reliably.