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Found 19 papers, 12 papers with code
Teaching CORnet Human fMRI Representations for Enhanced Model-Brain Alignment
To answer this question, this study proposed ReAlnet-fMRI, a model based on the SOTA vision model CORnet but optimized using human fMRI data through a multi-layer encoding-based alignment framework.
Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models
Based on the characteristics of the tasks and the strong dialogue-generation capabilities of LLMs, we propose RiC (Reasoning in Conversation), a method that focuses on solving subjective tasks through dialogue simulation.
DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results.
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages
While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages.
Towards Unified Alignment Between Agents, Humans, and Environment
We also conduct proof-of-concept studies by introducing realistic features to WebShop, including user profiles to demonstrate intentions, personalized reranking for complex environmental dynamics, and runtime cost statistics to reflect self-constraints.
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment
Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains.
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models
We propose symbol-to-language (S2L), a tuning-free method that enables large language models to solve symbol-related problems with information expressed in natural language.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
Self-Knowledge Guided Retrieval Augmentation for Large Language Models
Large language models (LLMs) have shown superior performance without task-specific fine-tuning.
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
There are two main challenges to leveraging retrieval-augmented methods for NKI tasks: 1) the demand for diverse relevance score functions and 2) the dilemma between training cost and task performance.
CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
Ranked #1 on
Word Similarity
on WS353
YATO: Yet Another deep learning based Text analysis Open toolkit
Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.
Can Offline Reinforcement Learning Help Natural Language Understanding?
Experimental results show that our RL pre-trained models can give close performance compared with the models using the LM training objective, showing that there exist common useful features across these two modalities.
Pre-Training a Graph Recurrent Network for Language Representation
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing.
Does Chinese BERT Encode Word Structure?
Contextualized representations give significantly improved results for a wide range of NLP tasks.
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Machine reading is a fundamental task for testing the capability of natural language understanding, which is closely related to human cognition in many aspects.
How Can BERT Help Lexical Semantics Tasks?
Contextualized embeddings such as BERT can serve as strong input representations to NLP tasks, outperforming their static embeddings counterparts such as skip-gram, CBOW and GloVe.
