Search Results for author: Chenhao Tan

Found 57 papers, 24 papers with code

Human-Centered Evaluation of Explanations

no code implementations NAACL (ACL) 2022 Jordan Boyd-Graber, Samuel Carton, Shi Feng, Q. Vera Liao, Tania Lombrozo, Alison Smith-Renner, Chenhao Tan

The NLP community are increasingly interested in providing explanations for NLP models to help people make sense of model behavior and potentially improve human interaction with models.

Clinical Notes Reveal Physician Fatigue

no code implementations5 Dec 2023 Chao-Chun Hsu, Ziad Obermeyer, Chenhao Tan

Finally, the model indicates that notes written about Black and Hispanic patients have 12% and 21% higher predicted fatigue than Whites -- larger than overnight vs. daytime differences.

Decision Making

Pragmatic Radiology Report Generation

1 code implementation28 Nov 2023 Dang Nguyen, Chacha Chen, He He, Chenhao Tan

When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it?

Ecologically Valid Explanations for Label Variation in NLI

1 code implementation20 Oct 2023 Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe

Human label variation, or annotation disagreement, exists in many natural language processing (NLP) tasks, including natural language inference (NLI).

Natural Language Inference valid

FLamE: Few-shot Learning from Natural Language Explanations

no code implementations13 Jun 2023 Yangqiaoyu Zhou, Yiming Zhang, Chenhao Tan

Natural language explanations have the potential to provide rich information that in principle guides model reasoning.

Classification Few-Shot Learning +1

Language of Bargaining

no code implementations12 Jun 2023 Mourad Heddaya, Solomon Dworkin, Chenhao Tan, Rob Voigt, Alexander Zentefis

Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining.

Entity-Based Evaluation of Political Bias in Automatic Summarization

1 code implementation3 May 2023 Karen Zhou, Chenhao Tan

Growing literature has shown that NLP systems may encode social biases; however, the political bias of summarization models remains relatively unknown.

Abstractive Text Summarization

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

no code implementations28 Apr 2023 Emre Kiciman, Robert Ness, Amit Sharma, Chenhao Tan

The causal capabilities of large language models (LLMs) is a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy.

Causal Discovery Common Sense Reasoning +2

Learning Human-Compatible Representations for Case-Based Decision Support

1 code implementation6 Mar 2023 Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan

Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.

Classification Decision Making +1

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

no code implementations24 Nov 2022 Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.

Object object-detection +2

Active Example Selection for In-Context Learning

1 code implementation8 Nov 2022 Yiming Zhang, Shi Feng, Chenhao Tan

For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5. 8\%$ improvement on average.

In-Context Learning

Probing Classifiers are Unreliable for Concept Removal and Detection

no code implementations8 Jul 2022 Abhinav Kumar, Chenhao Tan, Amit Sharma

Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc or adversarial methods will fail to remove the concept correctly.


Learning to Ignore Adversarial Attacks

no code implementations23 May 2022 Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton, Chenhao Tan

Despite the strong performance of current NLP models, they can be brittle against adversarial attacks.

Data Augmentation Test

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

no code implementations25 Apr 2022 Vivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao, Yunfeng Zhang, Chenhao Tan

Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples.

Open-Ended Question Answering

Machine Explanations and Human Understanding

1 code implementation8 Feb 2022 Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan

Our key result is that without assumptions about task-specific intuitions, explanations may potentially improve human understanding of model decision boundary, but they cannot improve human understanding of task decision boundary or model error.

Decision Making Open-Ended Question Answering

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

1 code implementation3 Feb 2022 Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh

Overall, we hope our work serves as a starting place for researchers and engineers to design interactive explainability systems.

BIG-bench Machine Learning

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

no code implementations21 Dec 2021 Vivian Lai, Chacha Chen, Q. Vera Liao, Alison Smith-Renner, Chenhao Tan

Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions.

Decision Making

What to Learn, and How: Toward Effective Learning from Rationales

1 code implementation Findings (ACL) 2022 Samuel Carton, Surya Kanoria, Chenhao Tan

Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i. e. subsets of input tokens) that justify their chosen labels, often in the form of intermediate or multitask supervision.

Decision-Focused Summarization

1 code implementation EMNLP 2021 Chao-Chun Hsu, Chenhao Tan

To evaluate our method (DecSum), we build a testbed where the task is to summarize the first ten reviews of a restaurant in support of predicting its future rating on Yelp.

On the Diversity and Limits of Human Explanations

no code implementations NAACL 2022 Chenhao Tan

A growing effort in NLP aims to build datasets of human explanations.

On Positivity Bias in Negative Reviews

1 code implementation ACL 2021 Madhusudhan Aithal, Chenhao Tan

Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality.


Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making

no code implementations13 Jan 2021 Han Liu, Vivian Lai, Chenhao Tan

Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance).

Decision Making Open-Ended Question Answering

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

2 code implementations10 Nov 2020 Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.

Causal Inference counterfactual +2

Evaluating and Characterizing Human Rationales

1 code implementation EMNLP 2020 Samuel Carton, Anirudh Rathore, Chenhao Tan

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.

Open-Ended Question Answering

Harnessing Explanations to Bridge AI and Humans

no code implementations16 Mar 2020 Vivian Lai, Samuel Carton, Chenhao Tan

Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive power.

Decision Making Medical Diagnosis

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

no code implementations14 Jan 2020 Vivian Lai, Han Liu, Chenhao Tan

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans.

Decision Making

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

3 code implementations6 Dec 2019 Divyat Mahajan, Chenhao Tan, Amit Sharma

For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world.

BIG-bench Machine Learning counterfactual +1

What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments

no code implementations1 Nov 2019 David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan

Explanations are central to everyday life, and are a topic of growing interest in the AI community.

Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification

1 code implementation IJCNLP 2019 Vivian Lai, Jon Z. Cai, Chenhao Tan

In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME.

Feature Importance General Classification +2

Gift Contagion in Online Groups: Evidence From Virtual Red Packets

no code implementations24 Jun 2019 Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang

Using data on 36 million online red packet gifts on a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.

Experimental Design Marketing

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

7 code implementations19 May 2019 Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.

BIG-bench Machine Learning counterfactual +1

Learning Fair Representations via an Adversarial Framework

1 code implementation30 Apr 2019 Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, Chunping Wang

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval.

Classification Fairness +1

No Permanent Friends or Enemies: Tracking Relationships between Nations from News

1 code implementation NAACL 2019 Xiaochuang Han, Eunsol Choi, Chenhao Tan

Understanding the dynamics of international politics is important yet challenging for civilians.

Measuring Online Debaters' Persuasive Skill from Text over Time

no code implementations TACL 2019 Kelvin Luu, Chenhao Tan, Noah A. Smith

We build on a widely used model of skill in two-player games and augment it with linguistic features of a debater{'}s content.

Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability

1 code implementation NeurIPS 2019 Brian Lubars, Chenhao Tan

To obtain an empirical understanding of human preferences in different tasks, we build a dataset of 100 tasks from academic papers, popular media portrayal of AI, and everyday life, and administer a survey based on our proposed framework.

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

no code implementations19 Nov 2018 Vivian Lai, Chenhao Tan

In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency.

BIG-bench Machine Learning Deception Detection +1

LSTMs Exploit Linguistic Attributes of Data

no code implementations WS 2018 Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. Smith

While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data.

Memorization Open-Ended Question Answering

"You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates

no code implementations23 Feb 2018 Chenhao Tan, Hao Peng, Noah A. Smith

We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situation.

Binary Classification

Neural Models for Documents with Metadata

3 code implementations ACL 2018 Dallas Card, Chenhao Tan, Noah A. Smith

Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information.

Topic Models Variational Inference

Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts

1 code implementation ACL 2017 Chenhao Tan, Dallas Card, Noah A. Smith

Combining two statistics --- cooccurrence within documents and prevalence correlation over time --- our approach reveals a number of different ways in which ideas can cooperate and compete.

All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement

no code implementations4 Mar 2015 Chenhao Tan, Lillian Lee

In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments.

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