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20232026
most citedCan Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations

21 citations · 40 across the 14 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.CL2025

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…