ConceptEdit: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning

Knowledge Editing (KE) aims to adjust a Large Language Model's (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurring the computational expense of re-training the entire model. However, editing commonsense knowledge still faces difficulties, including limited knowledge coverage in existing resources, the infeasibility of annotating labels for an overabundance of commonsense knowledge, and the strict knowledge formats of current editing methods. In this paper, we address these challenges by presenting ConceptEdit, a framework that integrates conceptualization and instantiation into the KE pipeline for LLMs to enhance their commonsense reasoning capabilities. ConceptEdit dynamically diagnoses implausible commonsense knowledge within an LLM using another verifier LLM and augments the source knowledge to be edited with conceptualization for stronger generalizability. Experimental results demonstrate that LLMs enhanced with ConceptEdit successfully generate commonsense knowledge with improved plausibility compared to other baselines and achieve stronger performance across multiple question answering benchmarks.

SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance

The offline-to-online (O2O) paradigm in reinforcement learning (RL) utilizes pre-trained models on offline datasets for subsequent online fine-tuning. However, conventional O2O RL algorithms typically require maintaining and retraining the large offline datasets to mitigate the effects of out-of-distribution (OOD) data, which limits their efficiency in exploiting online samples. To address this challenge, we introduce a new paradigm called SAMG: State-Action-Conditional Offline-to-Online Reinforcement Learning with Offline Model Guidance. In particular, rather than directly training on offline data, SAMG freezes the pre-trained offline critic to provide offline values for each state-action pair to deliver compact offline information. This framework eliminates the need for retraining with offline data by freezing and leveraging these values of the offline model. These are then incorporated with the online target critic using a Bellman equation weighted by a policy state-action-aware coefficient. This coefficient, derived from a conditional variational auto-encoder (C-VAE), aims to capture the reliability of the offline data on a state-action level. SAMG could be easily integrated with existing Q-function based O2O RL algorithms. Theoretical analysis shows good optimality and lower estimation error of SAMG. Empirical evaluations demonstrate that SAMG outperforms four state-of-the-art O2O RL algorithms in the D4RL benchmark.

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions

Entity- and event-level conceptualization, as fundamental elements of human cognition, plays a pivotal role in generalizable reasoning. This process involves abstracting specific instances into higher-level concepts and forming abstract knowledge that can be applied in unfamiliar or novel situations, which can enhance models' inferential capabilities and support the effective transfer of knowledge across various domains. Despite its significance, there is currently a lack of a systematic overview that comprehensively examines existing works in the definition, execution, and application of conceptualization to enhance reasoning tasks. In this paper, we address this gap by presenting the first comprehensive survey of 150+ papers, categorizing various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels. Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.