21 citations · 40 across the 14 of their papers we have counts for
15 papers
GTA: Generating Long-Horizon Tasks for Web Agents at Scale
Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey +4
Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable,…
The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering
Yefan Zhou, Yilun Zhou, Austin Xu +3
Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they may be under-critical and mis…
Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
Haoyi Qiu, Yilun Zhou, Pranav Narayanan Venkit +4
As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2…
DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou +3
Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing ci…
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization
Austin Xu, Yilun Zhou, Xuan-Phi Nguyen +2
To keep pace with the increasing pace of large language models (LLM) development, model output evaluation has transitioned away from time-consuming human evaluation to automatic ev…
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
Yilun Zhou, Austin Xu, Peifeng Wang +2
Scaling test-time computation, or affording a generator large language model (LLM) extra compute during inference, typically employs the help of external non-generative evaluators…
BingoGuard: LLM Content Moderation Tools with Risk Levels
Fan Yin, Philippe Laban, Xiangyu Peng +7
Malicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to as…
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…
Evaluating the Utility of Model Explanations for Model Development
Shawn Im, Jacob Andreas, Yilun Zhou
One of the motivations for explainable AI is to allow humans to make better and more informed decisions regarding the use and deployment of AI models. But careful evaluations are n…
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam +2
Large language models (LLMs) such as ChatGPT have demonstrated superior performance on a variety of natural language processing (NLP) tasks including sentiment analysis, mathematic…