Search Results for author:
Found 22 papers, 14 papers with code
No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity.
HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 136K data samples to support the fine-tuning, and an evaluation benchmark with 5 tasks and 9 datasets.
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models.
The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks.
Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning
Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise.
Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer
This paper presents a new method for end-to-end Video Question Answering (VideoQA), aside from the current popularity of using large-scale pre-training with huge feature extractors.
Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets.
Ranked #1 on
PAIR TRADING
on S&P 500 Pair Trading
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information.
Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
With a multiscale sampling, RMI iterates the interaction of appearance-motion information at each scale and the question embeddings to build the multilevel question-guided visual representations.
Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering
Targeting these issues, this paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA.
Inductive Topic Variational Graph Auto-Encoder for Text Classification
T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.
Representation Learning
Semi-Supervised Text Classification
+1
Mention-centered Graph Neural Network for Document-level Relation Extraction
How to build the dependency of entities from different sentences in a document remains to be a great challenge.
RDSGAN: Rank-based Distant Supervision Relation Extraction with Generative Adversarial Framework
Distant supervision has been widely used for relation extraction but suffers from noise labeling problem.
Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human.
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e. g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention.
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame.
Micro-Attention for Micro-Expression recognition
The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior.
Micro Expression Recognition
Micro-Expression Recognition
+1
Neural Sparse Topical Coding
Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications.
