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Ece Kamar

10 accepted papers

2024

Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models

ICLR 2024poster

We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constraint satisfaction problems and use this framework to investigate how the LLM interacts internally with factual constraints.…

2024

Teaching Language Models to Hallucinate Less with Synthetic Tasks

ICLR 2024poster

Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generation, even though all necessary information is included in context. However, optimizing to make LLMs hallucinate less is…

Cited by 31SourcePDFScholar
2023

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

ACL 2023long

Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. W…

Cited by 135SourcePDFScholar
2022

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

ACL 2022long

Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. To help mitigate these…

2021

Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions

AAAI 2021technical

AI-systems that model and interact with their users can up-date their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI-system, they may actually hurt the performance with respect to individual users. Prio…

Cited by 5SourcePDFScholar
2021

Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork

AAAI 2021technical

AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In su…

2021

Understanding Failures of Deep Networks via Robust Feature Extraction

CVPR 2021poster

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visu…

Cited by 87PDFcodeScholar
2020

A Multi-Objective Approach to Mitigate Negative Side Effects

IJCAI 2020poster

Agents operating in unstructured environments often create negative side effects (NSE) that may not be easy to identify at design time. We examine how various forms of human feedback or autonomous exploration can be used to learn a penalty function associated with NSE during system deployment. We f…

Cited by 0SourcePDFScholar
2020

SQuINTing at VQA Models: Introspecting VQA Models With Sub-Questions

CVPR 2020oral

Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasonin…

Cited by 85PDFScholar