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Elizabeth Daly

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6 papers

1 author row

NeurIPS Conference 2025 Conference Paper

BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks

Anna Sokol
Elizabeth Daly
Michael Hind
David Piorkowski
Xiangliang Zhang
Nuno Moniz
Nitesh Chawla

Large language models (LLMs) are powerful tools capable of handling diverse tasks. Comparing and selecting appropriate LLMs for specific tasks requires systematic evaluation methods, as models exhibit varying capabilities across different domains. However, finding suitable benchmarks is difficult given the many available options. This complexity not only increases the risk of benchmark misuse and misinterpretation but also demands substantial effort from LLM users, seeking the most suitable benchmarks for their specific needs. To address these issues, we introduce BenchmarkCards, an intuitive and validated documentation framework that standardizes critical benchmark attributes such as objectives, methodologies, data sources, and limitations. Through user studies involving benchmark creators and users, we show that BenchmarkCards can simplify benchmark selection and enhance transparency, facilitating informed decision-making in evaluating LLMs. Data & Code: github. com/SokolAnn/BenchmarkCards huggingface. co/datasets/ASokol/BenchmarkCards

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NeurIPS Conference 2024 Conference Paper

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia

Yufang Hou
Alessandra Pascale
Javier Carnerero-Cano
Tigran Tchrakian
Radu Marinescu
Elizabeth Daly
Inkit Padhi
Prasanna Sattigeri

Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated information. However, it remains unclear how LLMs handle knowledge conflicts arising from different augmented retrieved passages, especially when these passages originate from the same source and have equal trustworthiness. In this work, we conduct a comprehensive evaluation of LLM-generated answers to questions that have varying answers based on contradictory passages from Wikipedia, a dataset widely regarded as a high-quality pre-training resource for most LLMs. Specifically, we introduce WikiContradict, a benchmark consisting of 253 high-quality, human-annotated instances designed to assess the performance of LLMs in providing a complete perspective on conflicts from the retrieved documents, rather than choosing one answer over another, when augmented with retrieved passages containing real-world knowledge conflicts. We benchmark a diverse range of both closed and open-source LLMs under different QA scenarios, including RAG with a single passage, and RAG with 2 contradictory passages. Through rigorous human evaluations on a subset of WikiContradict instances involving 5 LLMs and over 3, 500 judgements, we shed light on the behaviour and limitations of these models. For instance, when provided with two passages containing contradictory facts, all models struggle to generate answers that accurately reflect the conflicting nature of the context, especially for implicit conflicts requiring reasoning. Since human evaluation is costly, wealso introduce an automated model that estimates LLM performance using a strong open-source language model, achieving an F-score of 0. 8. Using this automated metric, we evaluate more than 1, 500 answers from seven LLMs across all WikiContradict instances.

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NeurIPS Conference 2023 Conference Paper

Cookie Consent Has Disparate Impact on Estimation Accuracy

Erik Miehling
Rahul Nair
Elizabeth Daly
Karthikeyan Natesan Ramamurthy
Robert Redmond

Cookies are designed to enable more accurate identification and tracking of user behavior, in turn allowing for more personalized ads and better performing ad campaigns. Given the additional information that is recorded, questions related to privacy and fairness naturally arise. How does a user's consent decision influence how much the system can learn about their demographic and tastes? Is the impact of a user's consent decision on the recommender system's ability to learn about their latent attributes uniform across demographics? We investigate these questions in the context of an engagement-driven recommender system using simulation. We empirically demonstrate that when consent rates exhibit demographic-dependence, user consent has a disparate impact on the recommender agent's ability to estimate users' latent attributes. In particular, we find that when consent rates are demographic-dependent, a user disagreeing to share their cookie may counter-intuitively cause the recommender agent to know more about the user than if the user agreed to share their cookie. Furthermore, the gap in base consent rates across demographics serves as an amplifier: users from the lower consent rate demographic who agree to cookie sharing generally experience higher estimation errors than the same users from the higher consent rate demographic, and conversely for users who choose to disagree to cookie sharing, with these differences increasing in consent rate gap. We discuss the need for new notions of fairness that encourage consistency between a user's privacy decisions and the system's ability to estimate their latent attributes.

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NeurIPS Conference 2022 Conference Paper

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Dennis Wei
Rahul Nair
Amit Dhurandhar
Kush R. Varshney
Elizabeth Daly
Moninder Singh

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a supervised learning model from a reference model regarded as safe. We then show how interpretability facilitates this safety assessment. For models including decision trees, generalized linear and additive models, the maximum deviation can be computed exactly and efficiently. For tree ensembles, which are not regarded as interpretable, discrete optimization techniques can still provide informative bounds. For a broader class of piecewise Lipschitz functions, we leverage the multi-armed bandit literature to show that interpretability produces tighter (regret) bounds on the maximum deviation. We present case studies, including one on mortgage approval, to illustrate our methods and the insights about models that may be obtained from deviation maximization.

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IJCAI Conference 2021 Conference Paper

What Changed? Interpretable Model Comparison

Rahul Nair
Massimiliano Mattetti
Elizabeth Daly
Dennis Wei
Oznur Alkan
Yunfeng Zhang

We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business applications with periodically retrained models, an updated model may deviate from its predecessor for some segments without a change in overall accuracy. In automated ML systems, where several ML pipelines are generated, the top pipelines have comparable accuracy but may have more subtle differences. We present a method for interpretable comparison of binary classification models by approximating them with Boolean decision rules. We introduce stabilization conditions that allow for the two rule sets to be more directly comparable. A method is proposed to compare two rule sets based on their statistical and semantic similarity by solving assignment problems and highlighting changes. An empirical evaluation on several benchmark datasets illustrates the insights that may be obtained and shows that artificially induced changes can be reliably recovered by our method.

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JAIR Journal 2019 Journal Article

Computing Multi-Modal Journey Plans under Uncertainty

Adi Botea
Akihiro Kishimoto
Evdokia Nikolova
Stefano Braghin
Michele Berlingerio
Elizabeth Daly

Multi-modal journey planning, which allows multiple types of transport within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life, transport networks feature uncertainty. Yet, most approaches assume a deterministic environment, making plans more prone to failures such as missed connections and major delays in the arrival. This paper presents an approach to computing optimal contingent plans in multi-modal journey planning. The problem is modeled as a search in an and/or state space. We describe search enhancements used on top of the AO* algorithm. Enhancements include admissible heuristics, multiple types of pruning that preserve the completeness and the optimality, and a hybrid search approach with a deterministic and a nondeterministic search. We demonstrate an NP-hardness result, with the hardness stemming from the dynamically changing distributions of the travel time random variables. We perform a detailed empirical analysis on realistic transport networks from cities such as Montpellier, Rome and Dublin. The results demonstrate the effectiveness of our algorithmic contributions, and the benefits of contingent plans as compared to standard sequential plans, when the arrival and departure times of buses are characterized by uncertainty.

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