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Found 18 papers, 8 papers with code
Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs).
Entropy-based Exploration Conduction for Multi-step Reasoning
In large language model (LLM) reasoning, multi-step processes have proven effective for solving complex tasks.
Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
By tracing attention distribution over the image through out intermediate layers, we observe that successful spatial reasoning correlates strongly with the model's ability to align its attention distribution with actual object locations, particularly differing between familiar and unfamiliar spatial relationships.
Diversity-Oriented Data Augmentation with Large Language Models
Data augmentation is an essential technique in natural language processing (NLP) for enriching training datasets by generating diverse samples.
LEKA:LLM-Enhanced Knowledge Augmentation
Therefore, we design a knowledge augmentation method LEKA for knowledge transfer that actively searches for suitable knowledge sources that can enrich the target domain's knowledge.
Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues
We develop a novel and comprehensive framework for investigating how effective LLMs are at measuring and scoring empathy of responses in dialogues, and what methods can be employed to deepen our understanding of LLM scoring.
Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation
Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks.
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
In the financial field, precise risk assessment tools are essential for decision-making.
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning
Recent advances in large language models (LLMs) have demonstrated their potential in handling complex reasoning tasks, which are usually achieved by constructing a thought chain to guide the model to solve the problem with multi-step thinking.
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data.
Prototypical Reward Network for Data-Efficient RLHF
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs).
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
To address these limitations, we introduce the Retrieval Augmented Thought Tree (RATT), a novel thought structure that considers both overall logical soundness and factual correctness at each step of the thinking process.
Dynamic and Adaptive Feature Generation with LLM
The representation of feature space is a crucial environment where data points get vectorized and embedded for upcoming modeling.
Compression Represents Intelligence Linearly
We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly.
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge
In this work, we present a comprehensive review of the relevant algorithms to provide a general understanding of this booming research area.
FELM: Benchmarking Factuality Evaluation of Large Language Models
In this benchmark, we collect responses generated from LLMs and annotate factuality labels in a fine-grained manner.
Composing Parameter-Efficient Modules with Arithmetic Operations
In this paper, we propose to compose these parameter-efficient modules through linear arithmetic operations in the weight space, thereby integrating different module capabilities.
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.
