Publications

ToxiSight: Insights Towards Detected Chat Toxicity

Domenico Tullo

We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (voir plus)le AI (XAI) techniques, including token importance analysis, model output visualization, and attribution to the training dataset. It also provides insights through the closest positive and negative examples, facilitating a deeper understanding and potential correction of the training data. Additionally, the dashboard includes word sense analysis—particularly useful for new moderators—and offers free-text explanations for both positive and negative predictions. This multi-faceted approach enhances the interpretability and transparency of toxicity detection models.

2024-09-21

EMNLP/2024/Workshop/BlackBoxNLP (accepté)

openreview.net

ChainBuddy: An AI Agent System for Generating LLM Pipelines

jingyue zhang

Ian Arawjo

As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LL… (voir plus)M behavior on user-specific tasks and craft effective pipelines to do so. Many users struggle with where to start, often referred to as the"blank page"problem. ChainBuddy, an AI assistant for generating evaluative LLM pipelines built into the ChainForge platform, aims to tackle this issue. ChainBuddy offers a straightforward and user-friendly way to plan and evaluate LLM behavior, making the process less daunting and more accessible across a wide range of possible tasks and use cases. We report a within-subjects user study comparing ChainBuddy to the baseline interface. We find that when using AI assistance, participants reported a less demanding workload and felt more confident setting up evaluation pipelines of LLM behavior. We derive insights for the future of interfaces that assist users in the open-ended evaluation of AI.

2024-09-20

ArXiv (prépublication)

doi.org

arxiv.org

ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines

jingyue zhang

Ian Arawjo

2024-09-20

ArXiv (prépublication)

doi.org

arxiv.org

ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines

jingyue zhang

Ian Arawjo

2024-09-20

ArXiv (prépublication)

doi.org

arxiv.org

Development of small, cost‐efficient scintillating fiber detectors for automated synthesis of positron emission tomography radiopharmaceuticals

Hailey Ahn

Liam Carroll

Robert Hopewell

I-Huang Tsai

Dean Jolly

Gassan Massarweh

Shirin A. Enger

2024-09-20

Medical Physics (Lancaster) (publié)

doi.org

The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System

KIERNAN BRODA-MILIAN

Hanane Dagdougui

Ranwa Al Mallah

2024-09-20

International Conference on Machine Learning and Computing (publié)

doi.org

Diagnostic tests for infections in critically ill immunocompromised patients

Guillaume Dumas

Adrien Joseph

Lara Zafrani

2024-09-19

Intensive Care Medicine (publié)

doi.org

Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming

Shubham Gupta

Isaac Neri Gomez-Sarmiento

Faez Amjed Mezdari

Mirco Ravanelli

Cem Subakan

2024-09-19

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data

Jiaming Zhou

Abbas Ghaddar

Ge Zhang

Liheng Ma

Yaochen Hu

Soumyasundar Pal

Mark Coates

Bin Wang

Yingxue Zhang

Jianye Hao

Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with… (voir plus) complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential and limitations of using graph-based synthetic reasoning data as training signals to enhance LLMs' reasoning capabilities. Our extensive experiments, conducted on two established natural language reasoning tasks -- inductive reasoning and spatial reasoning -- demonstrate that supervised fine-tuning (SFT) with synthetic graph-based reasoning data effectively enhances LLMs' reasoning performance without compromising their effectiveness on other standard evaluation benchmarks.

2024-09-19

ArXiv (prépublication)

doi.org

arxiv.org

Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data

Jiaming Zhou

Abbas Ghaddar

Ge Zhang

Liheng Ma

Yaochen Hu

Soumyasundar Pal

Mark Coates