6 papers · 1 filter
CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
Zhenhao Chen, Yongqiang Chen, Chenxi Liu +7
Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention. Since scientific discovery fundamentally relies on uncovering causal relati…
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
Hongjian Zhou, Xinyu Zou, Jinge Wu +19
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increa…
A Language Model from 1913: Pretraining on Historical Text
Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber +5
While modern language models increasingly rely on ever-larger web corpora, we show that pretraining on historical text (e.g., pre-1913 text) in a data-constrained setting can produ…
The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
Bohang Sun, Max Zhu, Francesco Caso +5
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tok…
TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs
Arshia Hemmat, Philip Torr, Yongqiang Chen +1
Diffusion language models (D-LLMs) offer parallel denoising and bidirectional context, but hallucination detection for D-LLMs remains underexplored. Prior detectors developed for a…
TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models
Shenxu Chang, Junchi Yu, Weixing Wang +4
Diffusion large language models (D-LLMs) have recently emerged as a promising alternative to auto-regressive LLMs (AR-LLMs). However, the hallucination problem in D-LLMs remains un…