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Found 8 papers, 1 papers with code
A Survey on Self-Evolution of Large Language Models
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications.
EVIT: Event-Oriented Instruction Tuning for Event Reasoning
We conduct extensive experiments on event reasoning tasks on several datasets.
MEEL: Multi-Modal Event Evolution Learning
Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution.
ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs
However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM's analysis of the question, overlooking the rich cognitive potential inherent in the vast knowledge encapsulated in KGs.
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data.
EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.
A Guider Network for Multi-Dual Learning
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
