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- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate1,015
A multi-agent referee team called ChatEval is constructed to autonomously discuss and evaluate the quality of generated responses from different models on open-ended questions and traditional natural language generation (NLG) tasks, offering a human-mimicking evaluation process for reliable assessments.
- MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare485
This paper trains and releases two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community and demonstrates that language representations pretrained in the target domain improve the performance of mental health detection tasks.
- 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.
- RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation213
This paper proposes learning to Refine Query for Retrieval Augmented Generation (RQ-RAG), endeavoring to enhance the model by equipping it with capabilities for explicit rewriting, decomposition, and disambiguation, and demonstrates enhanced performance in handling complex, multi-hop QA datasets.
- AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis192
In conclusion, AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language speech and enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving efficient concept-level sentiment analysis.
- Graph Neural Networks with Generated Parameters for Relation Extraction176
A novel graph neural network with generated parameters (GP-GNNs), i.e. the transition matrices used in message passing procedure, are produced by a generator taking natural language sentences as inputs, to demonstrate that multi-hop reasoning mechanism yields significant improvements.
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- Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models158
This study proposes ProAttack, a novel and efficient method for performing clean-label backdoor attacks based on the prompt, which uses the prompt itself as a trigger, which does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.
- A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook142
This article introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks, and delves into the potential future directions behind the emergence of reasoning abilities within foundation models.
- Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives126
A curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty, and a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention.
- GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning97
Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv.
- GLGE: A New General Language Generation Evaluation Benchmark75
The General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks, is presented and a leaderboard with strong baselines including MASS, BART, and ProphetNet is built.
- Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks72
This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks, exploiting computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly reduced parameter size due to lesser degrees of freedom in the Hamilton product.
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- RikiNet: Reading Wikipedia Pages for Natural Question Answering59
This paper introduces a new model, called RikiNet, which reads Wikipedia pages for natural question answering, which is the first single model that outperforms the single human performance.
- AI Alignment: A Contemporary Survey54
This survey aims to provide a comprehensive yet beginner-friendly review of alignment research topics and discusses current and prospective governance practices adopted by governments, industry actors, and other third parties, aimed at managing existing and future AI risks.
- Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning53
A new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer and significantly outperforms five state-of-the-art methods.
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- Interactive Language Learning by Question Answering48
This work proposes and evaluates a set of baseline models for the QAit task that includes deep reinforcement learning agents, and shows that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.
- Tracking single-cell evolution using clock-like chromatin accessibility loci41
A method that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species and shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions.
- Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space40
A novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension, question generation, and question-answering natural language inference tasks.
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- Few-Shot Domain Adaptation with Polymorphic Transformers28
A Polymorphic Transformer (Polyformer), which can be incorporated into any DNN backbones for few-shot domain adaptation, and which can perform robustly on the target domain after being trained on a few annotated images.
- On Orthogonality Constraints for Transformers24
This paper studies orthogonality constraints for transformers, showing the effectiveness with empirical evidence from ten machine translation tasks and two dialogue generation tasks and close to the 29.7 BLEU achieved by the very competitive dynamic convolution.
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 2026-10-12. 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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