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Found 27 papers, 5 papers with code
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization.
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models.
SEAL: Safety Enhanced Trajectory Planning and Control Framework for Quadrotor Flight in Complex Environments
The emergence of dynamic obstacles would further worsen the situation.
Active Learning for Direct Preference Optimization
Direct preference optimization (DPO) is a form of reinforcement learning from human feedback (RLHF) where the policy is learned directly from preferential feedback.
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
By explicitly disentangling the optimization of visual understanding from task-specific alignment, MDGD preserves pre-trained visual knowledge while enabling efficient task adaptation.
From Selection to Generation: A Survey of LLM-based Active Learning
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training.
Interactive Visualization Recommendation with Hier-SUCB
For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting.
GUI Agents: A Survey
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction.
Personalized Multimodal Large Language Models: A Survey
We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly.
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
To overcome the reasoning heterogeneity and grounding problems, we leverage on-policy KG exploration and RL to model a KG policy that generates token-level likelihood distributions for LLM-generated chain-of-thought reasoning paths, simulating KG reasoning preference.
Personalization of Large Language Models: A Survey
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications.
A Survey of Small Language Models
We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques.
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search.
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation
Modeling temporal characteristics plays a significant role in the representation learning of audio waveform.
Self-Updatable Large Language Models with Parameter Integration
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, remains a substantial challenge.
Federated Large Language Models: Current Progress and Future Directions
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications.
Visual Prompting in Multimodal Large Language Models: A Survey
This paper presents the first comprehensive survey on visual prompting methods in MLLMs, focusing on visual prompting, prompt generation, compositional reasoning, and prompt learning.
CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models
Instruction tuning in multimodal large language models (MLLMs) aims to smoothly integrate a backbone LLM with a pre-trained feature encoder for downstream tasks.
Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation
Augmented by the proposed synthetic dataset, FUTGA is enabled to identify the music's temporal changes at key transition points and their musical functions, as well as generate detailed descriptions for each music segment.
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image.
Ranked #136 on
Visual Question Answering
on MM-Vet
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Based on the retrieved user-item interactions, the LLM can analyze shared and distinct preferences among users, and summarize the patterns indicating which types of users would be attracted by certain items.
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment.
An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model
At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks.
FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis.
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes
Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval.
