most citedMechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Learning with a Single Rollout via Pass@k Policy Optimization

Fengdi Che, Yang Liu, Lei Yu +4

Group Relative Policy Optimization (GRPO) typically requires multiple rollouts per prompt to estimate relative credit, incurring substantial sampling cost and exacerbating training…

cs.CL2026

See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance

Honghui Zhang, Anna Min, Chenmeinian Guo +9

World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a…

cs.CL2026

DynaWeb: Model-Based Reinforcement Learning of Web Agents

Hang Ding, Peidong Liu, Junqiao Wang +7

The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step towards general-purpose AI assistan…

cs.CL2024★ 1 cited

Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations

Lei Yu, Meng Cao, Jackie Chi Kit Cheung +1

State-of-the-art language models (LMs) sometimes generate non-factual hallucinations that misalign with world knowledge. To explore the mechanistic causes of these hallucinations,…

cs.CL2024

Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation

Meng Cao, Lei Shu, Lei Yu +4

Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of thes…