activity
20232026
most citedDecoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs

1 citations · 3 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2025

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…

cs.SD2025

SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models

Qiaolin Wang, Xilin Jiang, Linyang He +2

While large audio-language models (LALMs) have demonstrated state-of-the-art audio understanding, their reasoning capability in complex soundscapes still falls behind large vision-…

cs.CL2025

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…

cs.CL2025

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…