21 citations · 40 across the 14 of their papers we have counts for
6 papers · 1 filter
Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses
Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou +2
Large Language Model (LLM)-based applications are graduating from research prototypes to products serving millions of users, influencing how people write and consume information. A…
Direct Judgement Preference Optimization
Peifeng Wang, Austin Xu, Yilun Zhou +2
Auto-evaluation is crucial for assessing response quality and offering feedback for model development. Recent studies have explored training large language models (LLMs) as generat…
Shared Imagination: LLMs Hallucinate Alike
Yilun Zhou, Caiming Xiong, Silvio Savarese +1
Despite the recent proliferation of large language models (LLMs), their training recipes -- model architecture, pre-training data and optimization algorithm -- are often very simil…
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
Daking Rai, Yilun Zhou, Shi Feng +2
Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations. Rec…
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
Rithesh Murthy, Liangwei Yang, Juntao Tan +15
The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…
CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities
Yujun Mao, Yoon Kim, Yilun Zhou
Recent large language models (LLMs) have shown indications of mathematical reasoning ability on challenging competition-level problems, especially with self-generated verbalization…