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Found 21 papers, 9 papers with code
Position-Aware Depth Decay Decoding ($D^3$): Boosting Large Language Model Inference Efficiency
Experiments on large language models (\ie the Llama) with $7 \sim 70$ billion parameters show that $D^3$ can achieve an average 1. 5x speedup compared with the full-inference pipeline while maintaining comparable performance with nearly no performance drop ($<1\%$) on the GSM8K and BBH benchmarks.
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives
Recognizing the lack of multi-task datasets with diverse many-shot distributions, we develop the Many-Shot ICL Benchmark (ICL-50)-a large-scale benchmark of 50 tasks that cover shot numbers from 1 to 350 within sequences of up to 8, 000 tokens-for fine-tuning purposes.
RED: Effective Trajectory Representation Learning with Comprehensive Information
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation.
Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks.
CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
On the other hand, there is substantial noise existing in the news data leading to extracting effective information with difficulty.
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
These fine-tuned VLMs may still ignore the relationships between UI pages, neglect the roles of elements in page transitions and lack inter-UI understanding.
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications.
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents
With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction.
Simulating Financial Market via Large Language Model based Agents
In this paper, we propose \textbf{A}gent-based \textbf{S}imulated \textbf{F}inancial \textbf{M}arket (ASFM), which first constructs a simulated stock market with a real order matching system.
Facilitating Multi-Role and Multi-Behavior Collaboration of Large Language Models for Online Job Seeking and Recruiting
To this end, we propose MockLLM, a novel applicable framework that divides the person-job matching process into two modules: mock interview generation and two-sided evaluation in handshake protocol, jointly enhancing their performance through collaborative behaviors between interviewers and candidates.
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
360$^\circ$REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
The framework employs a novel 360$^\circ$ performance assessment method for multi-perspective performance evaluation with fine-grained assessment.
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas.
Not All Layers of LLMs Are Necessary During Inference
That is, not all layers of LLMs are necessary during inference.
Explainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
CharacterChat: Learning towards Conversational AI with Personalized Social Support
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
