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Found 12 papers, 1 papers with code
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
On the other hand, while large language models (LLMs), a popular solution for this task, demonstrate strong reasoning abilities, they struggle with the domain-specific complexities of personalized healthy dietary reasoning, and existing benchmarks fail to capture these challenges.
Granite Guardian
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM).
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs
With the increasing reliance on large language models (LLMs) for guidance in various fields, including laboratory settings, there is a growing concern about their reliability in critical safety-related decision-making.
Black-box Uncertainty Quantification Method for LLM-as-a-Judge
In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations.
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
LLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training.
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions
To address this issue, we conducted a scenario-based study (N=104) to systematically compare the impact of various design strategies for communicating factuality and source attribution on participants' ratings of trust, preferences, and ease in validating response accuracy.
Multi-Level Explanations for Generative Language Models
To address the challenges of text as output and long text inputs, we propose a general framework called MExGen that can be instantiated with different attribution algorithms.
Design Principles for Generative AI Applications
Generative AI applications present unique design challenges.
Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback
Millions of users come to online peer counseling platforms to seek support.
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification.
AutoDOViz: Human-Centered Automation for Decision Optimization
AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
