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Linyang He

7 accepted papers

2026

Decoding Inner Speech with an End-to-End Brain-to-Text Neural Interface

ICLR 2026poster

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 decode phonemes before assembling sentences with an n-gram language model (LM), preventing joint optimization of all stag…

Cited by 0SourceScholar
2025

Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement

NeurIPS 2025poster

Understanding how the human brain progresses from processing simple linguistic inputs to performing high-level reasoning is a fundamental challenge in neuroscience. While modern large language models (LLMs) are increasingly used to model neural responses to language, their internal representations a…

Cited by 0SourceScholar
2025

Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence

ACL 2025finding

This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradigms: psycholinguistic and neurolinguistic. Traditional psycholinguistic evaluations often reflect statistical rules that…

Cited by 0SourcePDFScholar
2025

Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations

EMNLP 2025

Transformer-based speech language models (SLMs) have significantly improved neural speech recognition and understanding. While existing research has examined how well SLMs encode shallow acoustic and phonetic features, the extent to which SLMs encode nuanced syntactic and conceptual features remains

Cited by 0SourcePDFScholar
2024

Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs

COLING 2024main

Inspired by cognitive neuroscience studies, we introduce a novel “decoding probing” method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the brain and its representations as “neural a…

Cited by 7SourcePDFScholar
2024

Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?

ICASSP 2024accepted

Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given their distinct training modalities, it remains unclear whether they correlate with the same neural aspects. We directly add…

Cited by 0SourceScholar