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Found 25 papers, 11 papers with code
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation
Large pretrained language models offer powerful generation capabilities, but cannot be reliably controlled at a sub-sentential level.
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities.
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance.
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.
ContraCLM: Contrastive Learning For Causal Language Model
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents.
Improving Factual Consistency of Abstractive Summarization via Question Answering
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
Supporting Clustering with Contrastive Learning
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on
Short Text Clustering
on AG News
Entity-level Factual Consistency of Abstractive Text Summarization
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.
Cross-Lingual Document Classification
Document Classification
+2
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to.
Topic Modeling with Wasserstein Autoencoders
To measure the diversity of the produced topics, we propose a simple topic uniqueness metric.
WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS
We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications.
Adaptive Classification for Prediction Under a Budget
Our novel bottom-up method first trains a high-accuracy complex model.
Comments on the proof of adaptive submodular function minimization
We point out an issue with Theorem 5 appearing in "Group-based active query selection for rapid diagnosis in time-critical situations".
Dynamic Model Selection for Prediction Under a Budget
Our objective is to minimize overall average cost without sacrificing accuracy.
Pruning Random Forests for Prediction on a Budget
We propose to prune a random forest (RF) for resource-constrained prediction.
Optimally Pruning Decision Tree Ensembles With Feature Cost
We propose a novel 0-1 integer program formulation for ensemble pruning.
Feature-Budgeted Random Forest
We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost.
Max-Cost Discrete Function Evaluation Problem under a Budget
We develop a broad class of \emph{admissible} impurity functions that admit monomials, classes of polynomials, and hinge-loss functions that allow for flexible impurity design with provably optimal approximation bounds.
