Search Results for author: Feng Nan

Found 25 papers, 11 papers with code

ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities

1 code implementation8 Aug 2024 Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, ZiRui Wang, Ruoming Pang

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

no code implementations29 Jul 2024 Tom Gunter, ZiRui Wang, Chong Wang, Ruoming Pang, Aonan Zhang, BoWen Zhang, Chen Chen, Chung-Cheng Chiu, David Qiu, Deepak Gopinath, Dian Ang Yap, Dong Yin, Feng Nan, Floris Weers, Guoli Yin, Haoshuo Huang, Jianyu Wang, Jiarui Lu, John Peebles, Ke Ye, Mark Lee, Nan Du, Qibin Chen, Quentin Keunebroek, Sam Wiseman, Syd Evans, Tao Lei, Vivek Rathod, Xiang Kong, Xianzhi Du, Yanghao Li, Yongqiang Wang, Yuan Gao, Zaid Ahmed, Zhaoyang Xu, Zhiyun Lu, Al Rashid, Albin Madappally Jose, Alec Doane, Alfredo Bencomo, Allison Vanderby, Andrew Hansen, Ankur Jain, Anupama Mann Anupama, Areeba Kamal, Bugu Wu, Carolina Brum, Charlie Maalouf, Chinguun Erdenebileg, Chris Dulhanty, Dominik Moritz, Doug Kang, Eduardo Jimenez, Evan Ladd, Fangping Shi, Felix Bai, Frank Chu, Fred Hohman, Hadas Kotek, Hannah Gillis Coleman, Jane Li, Jeffrey Bigham, Jeffery Cao, Jeff Lai, Jessica Cheung, Jiulong Shan, Joe Zhou, John Li, Jun Qin, Karanjeet Singh, Karla Vega, Kelvin Zou, Laura Heckman, Lauren Gardiner, Margit Bowler, Maria Cordell, Meng Cao, Nicole Hay, Nilesh Shahdadpuri, Otto Godwin, Pranay Dighe, Pushyami Rachapudi, Ramsey Tantawi, Roman Frigg, Sam Davarnia, Sanskruti Shah, Saptarshi Guha, Sasha Sirovica, Shen Ma, Shuang Ma, Simon Wang, Sulgi Kim, Suma Jayaram, Vaishaal Shankar, Varsha Paidi, Vivek Kumar, Xin Wang, Xin Zheng, Walker Cheng, Yael Shrager, Yang Ye, Yasu Tanaka, Yihao Guo, Yunsong Meng, Zhao Tang Luo, Zhi Ouyang, Alp Aygar, Alvin Wan, Andrew Walkingshaw, Andy Narayanan, Antonie Lin, Arsalan Farooq, Brent Ramerth, Colorado Reed, Chris Bartels, Chris Chaney, David Riazati, Eric Liang Yang, Erin Feldman, Gabriel Hochstrasser, Guillaume Seguin, Irina Belousova, Joris Pelemans, Karen Yang, Keivan Alizadeh Vahid, Liangliang Cao, Mahyar Najibi, Marco Zuliani, Max Horton, Minsik Cho, Nikhil Bhendawade, Patrick Dong, Piotr Maj, Pulkit Agrawal, Qi Shan, Qichen Fu, Regan Poston, Sam Xu, Shuangning Liu, Sushma Rao, Tashweena Heeramun, Thomas Merth, Uday Rayala, Victor Cui, Vivek Rangarajan Sridhar, Wencong Zhang, Wenqi Zhang, Wentao Wu, Xingyu Zhou, Xinwen Liu, Yang Zhao, Yin Xia, Zhile Ren, Zhongzheng Ren

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.

Language Modeling Language Modelling

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

1 code implementation25 Jan 2023 Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown

Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.

Natural Language Inference

Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization

no code implementations5 Aug 2021 Markus Dreyer, Mengwen Liu, Feng Nan, Sandeep Atluri, Sujith Ravi

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.

Abstractive Text Summarization

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

no code implementations27 Apr 2021 Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani, Feng Nan, Philip Resnik, Douglas Oard, Parminder Bhatia

The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.

Hallucination Informativeness +2

Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

1 code implementation25 Nov 2019 Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

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.

WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS

no code implementations ICLR Workshop LLD 2019 Ian Gemp, Ramesh Nallapati, Ran Ding, Feng Nan, Bing Xiang

We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.

Topic Models

Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget

no code implementations31 May 2017 Henghui Zhu, Feng Nan, Ioannis Paschalidis, Venkatesh Saligrama

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.

Decision Making Feature Engineering +2

Comments on the proof of adaptive submodular function minimization

no code implementations10 May 2017 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".

Active Learning Stochastic Optimization

Dynamic Model Selection for Prediction Under a Budget

no code implementations25 Apr 2017 Feng Nan, Venkatesh Saligrama

Our objective is to minimize overall average cost without sacrificing accuracy.

model Model Selection +1

Feature-Budgeted Random Forest

no code implementations20 Feb 2015 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 additional cost.

Prediction

Max-Cost Discrete Function Evaluation Problem under a Budget

no code implementations12 Jan 2015 Feng Nan, Joseph Wang, Venkatesh Saligrama

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.

General Classification

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