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Found 57 papers, 40 papers with code
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability
The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism.
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.
Apollo: An Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources.
Online Training of Large Language Models: Learn while chatting
Large Language Models(LLMs) have dramatically revolutionized the field of Natural Language Processing(NLP), offering remarkable capabilities that have garnered widespread usage.
Rethinking The Uniformity Metric in Self-Supervised Learning
Uniformity plays a crucial role in the assessment of learned representations, contributing to a deeper comprehension of self-supervised learning.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
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.
ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model
Recent advancements in Large Vision-Language Models (LVLMs) have enabled processing of multimodal inputs in language models but require significant computational resources for deployment, especially in edge devices.
Humans or LLMs as the Judge? A Study on Judgement Biases
Adopting human and large language models (LLM) as judges (\textit{a. k. a} human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention.
Pushing The Limit of LLM Capacity for Text Classification
The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks.
Silkie: Preference Distillation for Large Visual Language Models
This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context.
Ranked #17 on
Visual Question Answering
on MM-Vet
MLLM-Bench, Evaluating Multi-modal LLMs using GPT-4V
In the pursuit of Artificial General Intelligence (AGI), the integration of vision in language models has marked a significant milestone.
OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning
These findings offer a novel perspective on the role of outcome supervision in training value models for multi-step reasoning tasks and provide theoretical justification for its advantage in value estimation for guided decoding.
Ranked #42 on
Arithmetic Reasoning
on GSM8K
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
On Elastic Language Models
To this end, we propose an elastic language model (ElasticLM) that elastically adjusts the tradeoff according to the request stream.
Quantifying Self-diagnostic Atomic Knowledge in Chinese Medical Foundation Model: A Computational Analysis
Recent studies primarily focused on the quality of FMs evaluated by GPT-4 or their ability to pass medical exams, no studies have quantified the extent of self-diagnostic atomic knowledge stored in FMs' memory, which is the basis of foundation models to provide factual and reliable suggestions.
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations
Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
PlatoLM: Teaching LLMs via a Socratic Questioning User Simulator
The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT conversations, as evidenced by Vicuna.
CMB: A Comprehensive Medical Benchmark in Chinese
We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China.
On the Difference of BERT-style and CLIP-style Text Encoders
Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e. g., BERT, one of the representative models.
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics.
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities.
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
This gives us a hint that relational knowledge might not be redundant to the stored knowledge of PLMs, but rather be complementary.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors
ChatGPT has stimulated the research boom in the field of large language models.
Lifting the Curse of Capacity Gap in Distilling Language Models
However, when the capacity gap between the teacher and the student is large, a curse of capacity gap appears, invoking a deficiency in distilling LMs.
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.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
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.
Natural Language Reasoning, A Survey
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
Modular Retrieval for Generalization and Interpretation
Built upon the paradigm, we propose a retrieval model with modular prompt tuning named REMOP.
Adapting Pre-trained Language Models for Quantum Natural Language Processing
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a combination of quantum models and classical pre-trained neural networks.
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
Medical vision-and-language pre-training (Med-VLP) has shown promising improvements on many downstream medical tasks owing to its applicability to extracting generic representations from medical images and texts.
Document-level Relation Extraction with Relation Correlations
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem.
MorphTE: Injecting Morphology in Tensorized Embeddings
In the era of deep learning, word embeddings are essential when dealing with text tasks.
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
We believe this work facilitates the industry, as it saves enormous efforts and costs of deployment and increases the utility of computing resources.
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
To uncover the domain-general LM, we propose to identify domain-general parameters by playing lottery tickets (dubbed doge tickets).
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.
Exploring Extreme Parameter Compression for Pre-trained Language Models
A tiny version achieves $96. 7\%$ performance of BERT-base with $ {1}/{48} $ encoder parameters (i. e., less than 2M parameters excluding the embedding layer) and $2. 7 \times$ faster on inference.
Word2Fun: Modelling Words as Functions for Diachronic Word Representation
Word meaning may change over time as a reflection of changes in human society.
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.
On Position Embeddings in BERT
Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e. g. BERT) to model word order.
Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
A Multi-task Learning Framework for Opinion Triplet Extraction
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms.
Aspect-Based Sentiment Analysis
Aspect Sentiment Triplet Extraction
+2
Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training
Movie recommendation systems provide users with ranked lists of movies based on individual’s preferences and constraints.
Encoding word order in complex embeddings
The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions.
Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching
We prove that by a mapping (via the trace operator) on the high-dimensional matching matrix, a low-dimensional attention matrix can be derived.
CNM: An Interpretable Complex-valued Network for Matching
This paper seeks to model human language by the mathematical framework of quantum physics.
Semantic Hilbert Space for Text Representation Learning
To address this issue, we propose a new framework that models different levels of semantic units (e. g. sememe, word, sentence, and semantic abstraction) on a single \textit{Semantic Hilbert Space}, which naturally admits a non-linear semantic composition by means of a complex-valued vector word representation.
A Quantum Many-body Wave Function Inspired Language Modeling Approach
The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory.
Quantum-inspired Complex Word Embedding
A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words.
TextZoo, a New Benchmark for Reconsidering Text Classification
Text representation is a fundamental concern in Natural Language Processing, especially in text classification.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
