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Found 48 papers, 25 papers with code
GenCompareSum: a hybrid unsupervised summarization method using salience
To this end, we propose a hybrid, unsupervised, abstractive-extractive approach, in which we walk through a document, generating salient textual fragments representing its key points.
Ranked #1 on
Text Summarization
on S2ORC
Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model
In external validations, LEME excelled in long-form QA with a Rouge-L of 0. 19 (all p<0. 0001), ranked second in MCQ accuracy (0. 68; all p<0. 0001), and scored highest in EHR summarization and clinical QA (ranging from 4. 24 to 4. 83 out of 5 for correctness, completeness, and readability).
FMDLlama: Financial Misinformation Detection based on Large Language Models
The emergence of social media has made the spread of misinformation easier.
Selective Preference Optimization via Token-Level Reward Function Estimation
This method applies to any existing alignment datasets with response-level annotations and enables cost-efficient token selection with small-scale oracle models and training data.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge.
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
In this paper, we take a step forward to explore LLMs for tabular data synthesis and privacy protection, by introducing a new framework HARMONIC for tabular data generation and evaluation.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs.
Are Large Language Models True Healthcare Jacks-of-All-Trades? Benchmarking Across Health Professions Beyond Physician Exams
To fill this research gap, we introduce the Examinations for Medical Personnel in Chinese (EMPEC), a pioneering large-scale healthcare knowledge benchmark in traditional Chinese.
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society.
Enhancing Content-based Recommendation via Large Language Model
However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains?
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models
MetaAligner models multi-objective alignment into three stages: (1) dynamic objectives reformulation algorithm reorganizes traditional alignment datasets to supervise the model on performing flexible alignment across different objectives; (2) conditional weak-to-strong correction paradigm aligns the weak outputs of fixed policy models to approach strong outputs with higher preferences in the corresponding alignment objectives, enabling plug-and-play inferences on any policy models, which significantly reduces training costs and facilitates alignment on close-source policy models; (3) generalizable inference method flexibly adjusts target objectives by updating their text descriptions in the prompts, facilitating generalizable alignment to unseen objectives.
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 Lay Person's Guide to Biomedicine: Orchestrating Large Language Models
Moreover, automated methods that can effectively assess the `layness' of generated summaries are lacking.
Factual Consistency Evaluation of Summarisation in the Era of Large Language Models
Experiments on TreatFact suggest that both previous methods and LLM-based evaluators are unable to capture factual inconsistencies in clinical summaries, posing a new challenge for FC evaluation.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel 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.
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.
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs.
LAiW: A Chinese Legal Large Language Models Benchmark
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.
Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models
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.
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
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.
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023.
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks.
LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation
We create a human-annotated data set for evaluating automatic factuality metrics, LongSciVerify, which contains fine-grained factual consistency annotations for long document summaries from the scientific domain.
A scoping review on multimodal deep learning in biomedical images and texts
In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research.
Graph Contrastive Topic Model
Based on it, we propose the graph contrastive topic model (GCTM), which conducts graph contrastive learning (GCL) using informative positive and negative samples that are generated by the graph-based sampling strategy leveraging in-depth correlation and irrelevance among documents and words.
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.
Word Grounded Graph Convolutional Network
An inductive Word-grounded Graph Convolutional Network (WGCN) is proposed to learn word and document representations based on WGraph in a supervised manner.
A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations
The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery.
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models
In this paper, we present a systematic review of recent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to LLMs, to help understand the latest progress, challenges, and future directions.
Zero-shot Temporal Relation Extraction with ChatGPT
The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.
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.
Towards Interpretable Mental Health Analysis with Large Language Models
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
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.
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization
In this paper, we particularly explore ChatGPT's ability to evaluate factual inconsistency under a zero-shot setting by examining it on both coarse-grained and fine-grained evaluation tasks including binary entailment inference, summary ranking, and consistency rating.
Abstractive Text Summarization
Natural Language Inference
+3
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
We propose to extract medical facts of the input medical report, its gold summary, and candidate summaries based on the RadGraph schema and design the fact-guided reranker to efficiently incorporate the extracted medical facts for selecting the optimal summary.
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization
To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on citation graphs, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references.
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
Readability Controllable Biomedical Document Summarization
Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge.
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.
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization
Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization.
Ranked #10 on
Text Summarization
on Pubmed
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models.
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.
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
MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning
In this paper, we propose the conversational query rewriting model - MLR, which is a Multi-task model on sequence Labeling and query Rewriting.
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.
