28 citations · 147 across the 23 of their papers we have counts for
24 papers
Rethinking Self-Distillation for Multi-Teacher Capability Merging
Roy Xie, Dan Friedman, Feng Nan +7
Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-model post-training. Recent tren…
SO-Bench: A Structural Output Evaluation of Multimodal LLMs
Di Feng, Kaixin Ma, Feng Nan +9
Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schem…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Guoli Yin, Haoping Bai, Shuang Ma +21
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, whi…
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Jiarui Lu, Thomas Holleis, Yizhe Zhang +9
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation o…
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
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 serv…
ContraCLM: Contrastive Learning For Causal Language Model
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9
Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Markus Dreyer, Mengwen Liu, Feng Nan +2
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 documen…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
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. The f…
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani +4
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the tas…
Supporting Clustering with Contrastive Learning
Dejiao Zhang, Feng Nan, Xiaokai Wei +6
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often ove…
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang +5
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models t…
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
Yifan Gao, Henghui Zhu, Patrick Ng +7
In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system ne…
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Siamak Shakeri, Cicero Nogueira dos Santos, Henry Zhu +5
We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate bo…
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
Henghui Zhu, Feng Nan, Zhiguo Wang +2
Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of con…
Topic Modeling with Wasserstein Autoencoders
Feng Nan, Ran Ding, Ramesh Nallapati +1
We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on…
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Henghui Zhu, Feng Nan, Ioannis Paschalidis +1
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…
Adaptive Classification for Prediction Under a Budget
Feng Nan, Venkatesh Saligrama
We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction mo…
Comments on the proof of adaptive submodular function minimization
Feng Nan, Venkatesh Saligrama
We point out an issue with Theorem 5 appearing in "Group-based active query selection for rapid diagnosis in time-critical situations". Theorem 5 bounds the expected number of quer…
Dynamic Model Selection for Prediction Under a Budget
Feng Nan, Venkatesh Saligrama
We present a dynamic model selection approach for resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the inp…
Pruning Random Forests for Prediction on a Budget
Feng Nan, Joseph Wang, Venkatesh Saligrama
We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose prunin…
Optimally Pruning Decision Tree Ensembles With Feature Cost
Feng Nan, Joseph Wang, Venkatesh Saligrama
We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduc…
Feature-Budgeted Random Forest
Feng Nan, Joseph Wang, Venkatesh Saligrama
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 addit…
Max-Cost Discrete Function Evaluation Problem under a Budget
Feng Nan, Joseph Wang, Venkatesh Saligrama
We propose novel methods for max-cost Discrete Function Evaluation Problem (DFEP) under budget constraints. We are motivated by applications such as clinical diagnosis where a pati…