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Found 25 papers, 1 papers with code
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Until recently, search engines were the predominant method for people to access online information.
TnT-LLM: Text Mining at Scale with Large Language Models
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs
In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter.
AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach
Probing LLMs with varied iterations of a single question could reveal potential inconsistencies in their knowledge or functionality.
Developing a Framework for Auditing Large Language Models Using Human-in-the-Loop
An effective method is to probe the LLM using different versions of the same question.
From Prompt Engineering to Prompt Science With Human in the Loop
We need a more scientific approach to using LLMs in our research.
Effective Backdoor Mitigation Depends on the Pre-training Objective
In this work, we demonstrate that the efficacy of CleanCLIP in mitigating backdoors is highly dependent on the particular objective used during model pre-training.
Detecting Spurious Correlations via Robust Visual Concepts in Real and AI-Generated Image Classification
This undesirable property is the root cause of the manifestation of spurious correlations, which render models unreliable and prone to failure in the presence of distribution shifts.
Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models
Our new method is able to (1) identify weak decision boundaries for such classes; (2) construct search queries for Google as well as text for generating images through DALL-E 2 and Stable Diffusion; and (3) show how these newly captured training samples could alleviate population bias issue.
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations.
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
Artificial Intelligence in Career Counseling: A Test Case with ResumAI
It is difficult to find good resources or schedule an appointment with a career counselor to help with editing a resume for a specific role.
RecRec: Algorithmic Recourse for Recommender Systems
To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
Toward Connecting Speech Acts and Search Actions in Conversational Search Tasks
Then, the speech acts were fed to the model to predict the corresponding system-level search actions.
Taking Search to Task
For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist.
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
We evaluate RecXplainer on five real-world and large-scale recommendation datasets using five different kinds of recommender systems to demonstrate the efficacy of RecXplainer in capturing users' preferences over item attributes and using them to explain recommendations.
Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems
Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.
EGCR: Explanation Generation for Conversational Recommendation
To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets.
FAIR: Fairness-Aware Information Retrieval Evaluation
We believe our work opens up a new direction of pursuing a metric for evaluating and implementing the FAIR systems.
Users' Perception of Search Engine Biases and Satisfaction
We found out that users prefer results that are more consistent and relevant to the search queries.
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.
Facets of Fairness in Search and Recommendation
Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
Reading Protocol: Understanding what has been Read in Interactive Information Retrieval Tasks
In Interactive Information Retrieval (IIR) experiments the user's gaze motion on web pages is often recorded with eye tracking.
