Search Results for author: Yilun Zhou

Found 18 papers, 9 papers with code

CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities

no code implementations13 Jan 2024 Yujun Mao, Yoon Kim, Yilun Zhou

And while self-generated verbalizations of intermediate reasoning steps (i. e., chain-of-thought prompting) have been shown to be helpful, whether LLMs can make use of helpful side information such as problem-specific hints has not been investigated before.

Math Mathematical Reasoning

Evaluating the Utility of Model Explanations for Model Development

no code implementations10 Dec 2023 Shawn Im, Jacob Andreas, Yilun Zhou

One of the motivations for explainable AI is to allow humans to make better and more informed decisions regarding the use and deployment of AI models.

counterfactual Decision Making +1

Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations

no code implementations17 Oct 2023 Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, Leilani H. Gilpin

Through an extensive set of experiments, we find that ChatGPT's self-explanations perform on par with traditional ones, but are quite different from them according to various agreement metrics, meanwhile being much cheaper to produce (as they are generated along with the prediction).

Mathematical Reasoning Sentiment Analysis

Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques

1 code implementation27 May 2023 Daking Rai, Bailin Wang, Yilun Zhou, Ziyu Yao

Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs).

Domain Generalization Language Modelling +2

Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks

no code implementations17 Mar 2023 Yilun Zhou

For a subject that receives a negative model prediction (e. g., mortgage application denial), the CF explanations are similar instances but with positive predictions, which informs the subject of ways to improve.

counterfactual

Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)

no code implementations25 Jan 2023 Daking Rai, Yilun Zhou, Bailin Wang, Ziyu Yao

While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success.

Language Modelling Large Language Model +2

The Solvability of Interpretability Evaluation Metrics

1 code implementation18 May 2022 Yilun Zhou, Julie Shah

Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency.

ExSum: From Local Explanations to Model Understanding

1 code implementation NAACL 2022 Yilun Zhou, Marco Tulio Ribeiro, Julie Shah

Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment.

counterfactual

The Irrationality of Neural Rationale Models

1 code implementation NAACL (TrustNLP) 2022 Yiming Zheng, Serena Booth, Julie Shah, Yilun Zhou

We call for more rigorous and comprehensive evaluations of these models to ensure desired properties of interpretability are indeed achieved.

Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment

no code implementations14 Feb 2021 Ganesh Ghalme, Vineet Nair, Vishakha Patil, Yilun Zhou

Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare.

Decision Making Fairness

Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

1 code implementation29 Dec 2020 Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal

Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process.

Active Learning

Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example

1 code implementation19 Feb 2020 Serena Booth, Yilun Zhou, Ankit Shah, Julie Shah

To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx.

Domain Adaptation

Adversarially Guided Self-Play for Adopting Social Conventions

no code implementations16 Jan 2020 Mycal Tucker, Yilun Zhou, Julie Shah

Robotic agents must adopt existing social conventions in order to be effective teammates.

Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach

no code implementations9 Jan 2020 Serena Booth, Ankit Shah, Yilun Zhou, Julie Shah

In this paper, we consider the problem of exploring the prediction level sets of a classifier using probabilistic programming.

General Classification Probabilistic Programming

Learning Household Task Knowledge from WikiHow Descriptions

1 code implementation WS 2019 Yilun Zhou, Julie A. Shah, Steven Schockaert

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment.

On Memory Mechanism in Multi-Agent Reinforcement Learning

no code implementations11 Sep 2019 Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah

Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.

Multi-agent Reinforcement Learning reinforcement-learning +1

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

1 code implementation21 Feb 2019 Yilun Zhou, Steven Schockaert, Julie A. Shah

In this paper we instead propose to learn to predict path quality from crowdsourced human assessments.

Knowledge Graphs

Cannot find the paper you are looking for? You can Submit a new open access paper.