Papers by Ruiyu Zhang

4 papers
Soundwave: Less is More for Speech-Text Alignment in LLMs (2025.acl-long)

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Challenge: Existing end-to-end speech large language models rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth.
Approach: They propose a training strategy and a novel architecture to address representation space gap and sequence length inconsistency in speech and text.
Outcome: The proposed model outperforms other advanced speech LLMs in speech translation and AIR-Bench speech tasks with only a fraction of the training data.
Stimulate the Critical Thinking of LLMs via Debiasing Discussion (2025.emnlp-main)

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Challenge: Existing studies show that large language models (LLMs) are often prone to stance homogeneity and human preference biases when faced with conflicting perspectives.
Approach: They propose a novel two-stage training framework to address stance homogeneity bias and human preference bias by generating multi-model discussion datasets and optimizing reinforcement learning from human feedback to align with discussion correctness.
Outcome: The proposed framework reduces stance homogeneity bias and human preference bias and improves generalization capabilities on non-discussion scenarios and out-of-domain datasets.
FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)

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Challenge: Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation.
Approach: a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 .
Outcome: a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 .
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)

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Challenge: Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof .
Approach: They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement.
Outcome: The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models.

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