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- C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models963
C-Eval is presented, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context, and is anticipated to help analyze important strengths and shortcomings of foundation Models, and foster their development and growth for Chinese users.
- On the Sentence Embeddings from Pre-trained Language Models572
This paper proposes to transform the anisotropic sentence embedding distribution to a smooth and isotropic Gaussian distribution through normalizing flows that are learned with an unsupervised objective and achieves significant performance gains over the state-of-the-art sentence embeddings on a variety of semantic textual similarity tasks.
- Revisiting Self-Training for Neural Sequence Generation301
Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin.
- Choosing Transfer Languages for Cross-Lingual Learning282
This paper considers the task of automatically selecting optimal transfer languages as a ranking problem, and builds models that consider the aforementioned features to perform this prediction, and demonstrates that this model predicts good transfer languages much better than ad hoc baselines considering single features in isolation.
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- Mega: Moving Average Equipped Gated Attention256
This paper introduces Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism.
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- CTRLsum: Towards Generic Controllable Text Summarization165
CTRLsum is a generic framework to control generated summaries through a set of keywords, comparable or better than strong pretrained systems in a standard, unconstrained summarization setting.
- 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.
- K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization142
The first-ever LLM in geoscience, K2, is presented alongside a suite of resources developed to further promote LLM research within geoscience, and a protocol is shared that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce.
- Efficient Nearest Neighbor Language Models129
The recently proposed k-nearest neighbors language model is taken as an example, and methods to improve its efficiency along various dimensions are explored, able to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
- StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing103
StructVAE is introduced, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances, and outperforms strong supervised models.
- A Survey of Reasoning with Foundation Models73
This paper introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks and discusses the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning.
- A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text73
A simple fix for posterior collapse is investigated which yields surprisingly effective results and is used to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling.
- ATP homeostasis and signaling in plants64
Recent advances in understanding the regulatory control of the cellular ATP pool are reviewed and possible interactions among key regulators of ATP-pool homeostasis and crosstalk between iATP/eATP signaling pathways are discussed.
- Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation56
This work introduces Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth.
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- Efficient Correlated Topic Modeling with Topic Embedding49
A new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors is proposed, enabling efficient inference in the low-dimensional embedding space.
- Unsupervised Learning of Syntactic Structure with Invertible Neural Projections45
A novel generative model is proposed that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior so long as the prior is well-behaved.
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- High-accuracy deep ANN-to-SNN conversion using quantization-aware training framework and calcium-gated bipolar leaky integrate and fire neuron35
A calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model is proposed to better approximate the functions of the ReLU neurons widely adopted in ANNs and a quantization-aware training (QAT)-based framework leveraging an off-the-shelf QAT toolkit for easy ANN-to-SNN conversion is proposed.
- CompSNN: A lightweight spiking neural network based on spatiotemporally compressive spike features33
This paper proposes efficient spatiotemporally compressive spike features and presents a lightweight SNN framework that includes a feature extraction layer to extract such compressive features and has a potential to be applied in low-cost or resource-limited edge computing systems and embedded devices.
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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 2026-10-13. 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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