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Works154 from public data
- 526
- Humans or LLMs as the Judge? A Study on Judgement Bias436
A novel framework that is free from referencing groundtruth annotations for investigating bias and vulnerability of human- and LLM-as-a-judge, as well as the urgency of developing robust evaluation systems is proposed.
- Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models263
A comprehensive and challenging benchmark specifically designed to assess LLMs' mathematical reasoning at the Olympiad level, which shows that even the most advanced models, OpenAI o1-mini and OpenAI o1-preview, struggle with highly challenging Olympiad-level problems, highlighting significant challenges in Olympiad-level mathematical reasoning.
- Pre-trained Language Models in Biomedical Domain: A Systematic Survey256
The motivations of PLMs in the biomedical domain are discussed and the key concepts of pre-trained language models are introduced and a taxonomy of existing biomedical PLMs is proposed that categorizes them from various perspectives systematically.
- FinBen: A Holistic Financial Benchmark for Large Language Models220
FinBen is introduced, the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks, covering seven critical aspects: information extraction (IE), textual analysis, question answering (QA), text generation, risk management, forecasting, and decision-making.
- 206
- ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling196
The research team proposed OR-Instruct, a semiautomated data synthesis framework that generates high-quality training data tailored to specific optimization modeling requirements, and introduced IndustryOR, the first benchmark for evaluating LLMs’ performance on real-world OR problems.
- Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale150
This work refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) in an ‘unblinded’ capacity to denoise and reformat the data, resulting in the creation of the **PubMedVision** dataset with 1.3 million medical VQA samples.
- HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs142
The developed model, HuatuoGPT-II, has shown state-of-the-art performance in Chinese medicine domain on a number of benchmarks, and outperforms proprietary models like ChatGPT and GPT-4 in some aspects, especially in Traditional Chinese Medicine.
- A Multi-task Learning Framework for Opinion Triplet Extraction141
A novel view of ABSA as an opinion triplet extraction task is presented, and a multi-task learning framework to jointly extract aspect terms and opinion terms is proposed, and simultaneously parses sentiment dependencies between them with a biaffine scorer.
- Natural Language Reasoning, A Survey133
A clearer view of Natural Language Reasoning (NLR) in the field of Natural Language Processing (NLP) is proposed, both conceptually and practically, and defeasible reasoning is introduced as one of the most important future directions in NLR research.
- Detection of subtype blood cells using deep learning130
A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells, and the results show that the proposed model provide better results in terms of evaluation parameters.
- Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models123
This work introduces Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs) that exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines.
- A quantum-inspired multimodal sentiment analysis framework116
A Quantum-inspired Multimodal Sentiment Analysis (QMSA) framework that aims to fill the “semantic gap” and model the correlations between different modalities via density matrix and significantly outperforms a wide range of baselines and state-of-the-art methods.
- Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias114
This paper presents a novel framework named Unifying Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC), designed to integrate multimodal medical data from the two most prevalent languages, English and Spanish, and proposes Cross-lingual Text Alignment Regularization (CTR) to explicitly unify cross-lingUAL semantic representations of medical reports originating from diverse language communities.
- End-to-End Quantum-like Language Models with Application to Question Answering103
A Neural Network based Quantum-like Language Model (NNQLM) is developed and applied to Question Answering, which represents a sentence and encodes a mixture of semantic subspaces and can be integrated into neural network architectures.
- 97
- CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations92
A complex-valued fuzzy network is proposed by leveraging the mathematical formalisms of quantum theory and fuzzy logic to address the intrinsic vagueness and uncertainty of human language in emotional expression and understanding in sarcasm detection.
- DialogueLLM: Context and emotion knowledge-tuned large language models for emotion recognition in conversations85
DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning foundation large language models, is proposed, a context-aware model, which can accurately capture the dynamics of emotions throughout the dialogue.
- CNM: An Interpretable Complex-valued Network for Matching83
The proposed complex-valued network for matching (CNM) achieves comparable performances to strong CNN and RNN baselines on two benchmarking question answering (QA) datasets.
- 65
- What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability60
The potential of QP as a mathematical framework is explored and a QP driven multi-task (QPM) learning framework is proposed, which involves a complex-valued multi-modal representation encoder, a quantum-like fusion network and a quantum measurement mechanism.
- 57
- Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts56
This work unify the input format by introducing visual and textual prompts, which serve as DETR-like queries that assist in extracting features when one of the modalities is missing, and proposes an effective yet straightforward scheme named PTUnifier to unify the two types.
- 53
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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