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Siyuan Liu

20 papers hereh-index 325 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author13

Across the 18 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.LG4
  • cs.AI3
  • cs.CV2
  • cs.CR1
  • cs.DC1
same name
  • Siyuan Liu — 15 papers, h 11
  • Siyuan Liu — 15 papers, h 9
  • Siyuan Liu — 12 papers, h 5
  • Siyuan Liu — 10 papers, h 8
  • Siyuan Liu — 5 papers, h 8
  • Siyuan Liu — 5 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedDiscourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems

1 citations · 2 across the 18 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation

Kanghui Tian, Siyuan Liu, Tianxiang Jiang +8

More privileged information does not always make a better teacher. We study this tension in on-policy self-distillation (OPSD), where a self-teacher scores the student's own rollou…

cs.LG2026

How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization

Xinyi Wu, Siyuan Liu, Ali Jadbabaie

Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what det…

cs.LG2026

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning

Siyuan Liu, Tinghong Chen, Xinghan Li +2

Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has studied the effect of selecting da…

cs.LG2025

Two-Stage Regularization-Based Structured Pruning for LLMs

Mingkuan Feng, Jinyang Wu, Siyuan Liu +7

The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pru…

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