1 citations · 3 across the 14 of their papers we have counts for
11 papers · 1 filter
The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese
Siyuan Song, Zhiheng Qian, Yunhao Zhang +11
This paper presents the first ChineseBabyLM Challenge, organized as part of NLPCC 2026. The challenge asked participants to train language models from scratch using no more than 10…
Prune, Interpret, Evaluate: A Cross-Layer Transcoder-Native Framework for Efficient Circuit Discovery via Feature Attribution
Qinhao Chen, Linyang He, Nima Mesgarani
Existing feature-interpretation pipelines typically operate on uniformly sampled units or exhaustive feature sets, incurring massive costs on units irrelevant to target behaviors.…
LiveMathematicianBench: A Live Benchmark for Research-Level Mathematical Reasoning with Proof Sketches
Linyang He, Qiyao Yu, Hanze Dong +5
Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a central question in artificial intelligen…
LLMs Think with DAGs: Probing the Latent Graph Structure of Reasoning
Tianjun Zhong, Linyang He, Ziyang Li +1
Large language models often externalize reasoning as linear chains, but many reasoning problems can be more naturally modeled as directed acyclic graphs (DAGs), where intermediate…
A cross-species neural foundation model for end-to-end speech decoding
Yizi Zhang, Linyang He, Chaofei Fan +9
Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
Jaap Jumelet, Abdellah Fourtassi, Akari Haga +23
We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally pla…
Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement
Linyang He, Tianjun Zhong, Richard Antonello +3
Understanding how the human brain progresses from processing simple linguistic inputs to performing high-level reasoning is a fundamental challenge in neuroscience. While modern la…
Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations
Linyang He, Qiaolin Wang, Xilin Jiang +1
Transformer-based speech language models (SLMs) have significantly improved neural speech recognition and understanding. While existing research has examined how well SLMs encode s…
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs
Linyang He, Ercong Nie, Sukru Samet Dindar +10
We introduce XCOMPS in this work, a multilingual conceptual minimal pair dataset covering 17 languages. Using this dataset, we evaluate LLMs' multilingual conceptual understanding…
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence
Linyang He, Ercong Nie, Helmut Schmid +3
This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradig…
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs
Linyang He, Peili Chen, Ercong Nie +2
Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in…