Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domain setting, where all data come from the same domain (e.g., the same dataset). However, many real-world tasks often involve multiple domains. For example, in visual recognition, it is often desirable to train an image classifier that works across different environments (e.g., different backgrounds), where images from each environment constitute one domain. Such a multi-domain AL setting is challenging for prior methods because they (1) ignore the similarity among different domains when assigning labeling budget and (2) fail to handle distribution shift of data across different domains. In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain. Our theoretical analysis shows that our method achieves a better error bound compared to current AL methods. Our empirical results demonstrate that our approach significantly outperforms the state-of-the-art AL methods on both synthetic and real-world multi-domain datasets. Code is available at https://github.com/Wang-ML-Lab/multi-domain-active-learning.

Natural Counterfactuals With Necessary Backtracking

Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions. While Judea Pearl's influential approach is theoretically elegant, its generation of a counterfactual scenario often requires interventions that are too detached from the real scenarios to be feasible. In response, we propose a framework of natural counterfactuals and a method for generating counterfactuals that are natural with respect to the actual world's data distribution. Our methodology refines counterfactual reasoning, allowing changes in causally preceding variables to minimize deviations from realistic scenarios. To generate natural counterfactuals, we introduce an innovative optimization framework that permits but controls the extent of backtracking with a naturalness criterion. Empirical experiments indicate the effectiveness of our method.

Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents

Recent advancements on Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, since LLM's content generation process is hardly controllable, current LLM-based agents frequently generate invalid or non-executable plans, which jeopardizes the performance of the generated plans and corrupts users' trust in LLM-based agents. In response, this paper proposes a novel ``Formal-LLM'' framework for LLM-based agents by integrating the expressiveness of natural language and the precision of formal language. Specifically, the framework allows human users to express their requirements or constraints for the planning process as an automaton. A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable. We conduct experiments on both benchmark tasks and practical real-life tasks, and our framework achieves over 50% overall performance increase, which validates the feasibility and effectiveness of employing Formal-LLM to guide the plan generation of agents, preventing the agents from generating invalid and unsuccessful plans. Further, more controllable LLM-based agents can facilitate the broader utilization of LLM in application scenarios where high validity of planning is essential. The work is open-sourced at https://github.com/agiresearch/Formal-LLM.

Hierarchical Continual Reinforcement Learning via Large Language Model

The ability to learn continuously in dynamic environments is a crucial requirement for reinforcement learning (RL) agents applying in the real world. Despite the progress in continual reinforcement learning (CRL), existing methods often suffer from insufficient knowledge transfer, particularly when the tasks are diverse. To address this challenge, we propose a new framework, Hierarchical Continual reinforcement learning via large language model (Hi-Core), designed to facilitate the transfer of high-level knowledge. Hi-Core orchestrates a twolayer structure: high-level policy formulation by a large language model (LLM), which represents agenerates a sequence of goals, and low-level policy learning that closely aligns with goal-oriented RL practices, producing the agent's actions in response to the goals set forth. The framework employs feedback to iteratively adjust and verify highlevel policies, storing them along with low-level policies within a skill library. When encountering a new task, Hi-Core retrieves relevant experience from this library to help to learning. Through experiments on Minigrid, Hi-Core has demonstrated its effectiveness in handling diverse CRL tasks, which outperforms popular baselines.

Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design. However, problem remains on how to unify the CE and MIMODet by optimizing AI's structure to achieve near optimal detection performance such as widely considered QR with M-algorithm (QRM) that can perform close to the maximum likelihood (ML) detector. In this paper, we propose an AI receiver that connects CE and MIMODet as an unified architecture. As a merit, CE and MIMODet only adopt structural input features and conventional neural networks (NN) to perform end-to-end (E2E) training offline. Numerical results show that, by adopting a simple super-resolution based convolutional neural network (SRCNN) as channel estimator and domain knowledge enhanced graphical neural network (GNN) as detector, the proposed QRM enhanced GNN receiver (QRMNet) achieves comparable block error rate (BLER) performance to near-optimal baseline detectors.

TransTroj: Transferable Backdoor Attacks to Pre-trained Models via Embedding Indistinguishability

Pre-trained models (PTMs) are extensively utilized in various downstream tasks. Adopting untrusted PTMs may suffer from backdoor attacks, where the adversary can compromise the downstream models by injecting backdoors into the PTM. However, existing backdoor attacks to PTMs can only achieve partially task-agnostic and the embedded backdoors are easily erased during the fine-tuning process. In this paper, we propose a novel transferable backdoor attack, TransTroj, to simultaneously meet functionality-preserving, durable, and task-agnostic. In particular, we first formalize transferable backdoor attacks as the indistinguishability problem between poisoned and clean samples in the embedding space. We decompose the embedding indistinguishability into pre- and post-indistinguishability, representing the similarity of the poisoned and reference embeddings before and after the attack. Then, we propose a two-stage optimization that separately optimizes triggers and victim PTMs to achieve embedding indistinguishability. We evaluate TransTroj on four PTMs and six downstream tasks. Experimental results show that TransTroj significantly outperforms SOTA task-agnostic backdoor attacks (18%$\sim$99%, 68% on average) and exhibits superior performance under various system settings. The code is available at https://github.com/haowang-cqu/TransTroj .

Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets

This paper studies the synergy of solar-battery energy storage system (BESS) and develops a viable strategy for the BESS to unlock its economic potential by serving as a backup to reduce solar curtailments while also participating in the electricity market. We model the real-time bidding of the solar-battery system as two Markov decision processes for the solar farm and the BESS, respectively. We develop a novel deep reinforcement learning (DRL) algorithm to solve the problem by leveraging attention mechanism (AC) and multi-grained feature convolution to process DRL input for better bidding decisions. Simulation results demonstrate that our AC-DRL outperforms two optimization-based and one DRL-based benchmarks by generating 23%, 20%, and 11% higher revenue, as well as improving curtailment responses. The excess solar generation can effectively charge the BESS to bid in the market, significantly reducing solar curtailments by 76% and creating synergy for the solar-battery system to be more viable.

Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation

We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge probing. However, LLMs often fail to express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, Dreamcatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.

Driving Towards Inclusion: Revisiting In-Vehicle Interaction in Autonomous Vehicles

This paper presents a comprehensive literature review of the current state of in-vehicle human-computer interaction (HCI) in the context of self-driving vehicles, with a specific focus on inclusion and accessibility. This study's aim is to examine the user-centered design principles for inclusive HCI in self-driving vehicles, evaluate existing HCI systems, and identify emerging technologies that have the potential to enhance the passenger experience. The paper begins by providing an overview of the current state of self-driving vehicle technology, followed by an examination of the importance of HCI in this context. Next, the paper reviews the existing literature on inclusive HCI design principles and evaluates the effectiveness of current HCI systems in self-driving vehicles. The paper also identifies emerging technologies that have the potential to enhance the passenger experience, such as voice-activated interfaces, haptic feedback systems, and augmented reality displays. Finally, the paper proposes an end-to-end design framework for the development of an inclusive in-vehicle experience, which takes into consideration the needs of all passengers, including those with disabilities, or other accessibility requirements. This literature review highlights the importance of user-centered design principles in the development of HCI systems for self-driving vehicles and emphasizes the need for inclusive design to ensure that all passengers can safely and comfortably use these vehicles. The proposed end-to-end design framework provides a practical approach to achieving this goal and can serve as a valuable resource for designers, researchers, and policymakers in this field.

Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Conditional Interpretations

Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However, (1) they often fail to capture the high-order, nonlinear interaction between concepts, e.g., correcting a predicted concept (e.g., "yellow breast") does not help correct highly correlated concepts (e.g., "yellow belly"), leading to suboptimal final accuracy; (2) they cannot naturally quantify the complex conditional dependencies between different concepts and class labels (e.g., for an image with the class label "Kentucky Warbler" and a concept "black bill", what is the probability that the model correctly predicts another concept "black crown"), therefore failing to provide deeper insight into how a black-box model works. In response to these limitations, we propose Energy-based Concept Bottleneck Models (ECBMs). Our ECBMs use a set of neural networks to define the joint energy of candidate (input, concept, class) tuples. With such a unified interface, prediction, concept correction, and conditional dependency quantification are then represented as conditional probabilities, which are generated by composing different energy functions. Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations. Empirical results show that our approach outperforms the state-of-the-art on real-world datasets.