Search Results for author:
Found 19 papers, 4 papers with code
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.
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
We employ a probing technique to extract representations from different layers of the model and apply these to classification tasks.
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
Towards Uncovering How Large Language Model Works: An Explainability Perspective
Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque.
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback
The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency.
The Impact of Reasoning Step Length on Large Language Models
Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.
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.
Mitigating Shortcuts in Language Models with Soft Label Encoding
Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks.
Natural Language Understanding
Out-of-Distribution Generalization
Explainability for Large Language Models: A Survey
For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge.
One-Shot Pruning for Fast-adapting Pre-trained Models on Devices
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks.
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.
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning
PlugMed is equipped with two modules, the prompt generation (PG) module and the response ranking (RR) module, to enhances LLMs' dialogue strategies for improving the specificity of the dialogue.
Does Continual Learning Equally Forget All Parameters?
In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL.
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
To address these challenges, we create a small model for a new task from the pruned models of similar tasks.
Vote for Nearest Neighbors Meta-Pruning of Self-Supervised Networks
Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?
Massive Self-Assembly in Grid Environments
Self-assembly plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains.
Multiagent Systems Distributed, Parallel, and Cluster Computing Robotics
Extract Local Inference Chains of Deep Neural Nets
In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.
Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based Human Collective Intelligence
And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process.
