Search Results for author: Jimin Huang

Found 18 papers, 10 papers with code

MetaAligner: Conditional Weak-to-Strong Correction for Generalizable Multi-Objective Alignment of Language Models

no code implementations25 Mar 2024 Kailai Yang, Zhiwei Liu, Qianqian Xie, Tianlin Zhang, Nirui Song, Jimin Huang, Ziyan Kuang, Sophia Ananiadou

Recent advancements in large language models (LLMs) aim to tackle heterogeneous human expectations and values via multi-objective preference alignment.

In-Context Learning

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks

3 code implementations10 Mar 2024 Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Wanlong Yu, Jimin Huang, Qianqian Xie

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

no code implementations26 Feb 2024 Mengxi Xiao, Qianqian Xie, Ziyan Kuang, Zhicheng Liu, Kailai Yang, Min Peng, Weiguang Han, Jimin Huang

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.

Me LLaMA: Foundation Large Language Models for Medical Applications

1 code implementation20 Feb 2024 Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xingyu Zhou, Huan He, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian

This study introduces Me LLaMA, a medical LLM family that includes foundation models - Me LLaMA 13/70B, along with their chat-enhanced versions - Me LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.

Few-Shot Learning

Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English

2 code implementations12 Feb 2024 Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro Lopez-Lira, Xiao-Yang Liu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie

We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.

LAiW: A Chinese Legal Large Language Models Benchmark

1 code implementation9 Oct 2023 Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia, Qianqian Xie, Yifang Zhang, Weiguang Han, Wei Tian, Hao Wang

Through automated evaluation of current general and legal domain LLMs on our benchmark, we indicate that these LLMs may not align with the logic of legal practice.

Information Retrieval

Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models

1 code implementation2 Oct 2023 Chenhan Yuan, Qianqian Xie, Jimin Huang, Sophia Ananiadou

In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction.

Attribute Instruction Following +1

Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models

1 code implementation1 Oct 2023 Duanyu Feng, Yongfu Dai, Jimin Huang, Yifang Zhang, Qianqian Xie, Weiguang Han, Zhengyu Chen, Alejandro Lopez-Lira, Hao Wang

We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.

Decision Making Language Modelling +1

PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

2 code implementations8 Jun 2023 Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, Jimin Huang

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.

Conversational Question Answering Language Modelling +5

The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

no code implementations10 Apr 2023 Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, Jimin Huang

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

no code implementations1 Apr 2023 Weiguang Han, Jimin Huang, Qianqian Xie, Boyi Zhang, Yanzhao Lai, Min Peng

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.

PAIR TRADING reinforcement-learning +1

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

1 code implementation25 Jan 2023 Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng, Yanzhao Lai, Jimin Huang

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.

Hierarchical Reinforcement Learning PAIR TRADING +2

GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization

no code implementations COLING 2022 Qianqian Xie, Jimin Huang, Tulika Saha, Sophia Ananiadou

Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization.

Contrastive Learning Extractive Summarization +3

Neural Sparse Topical Coding

no code implementations ACL 2018 Min Peng, Qianqian Xie, Yanchun Zhang, Hua Wang, Xiuzhen Zhang, Jimin Huang, Gang Tian

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.

Language Modelling Topic Models +1

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