Papers by Xinyan Jiang

5 papers
AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters (2026.acl-long)

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Challenge: Existing methods for large reasoning models have improved efficiency but still face limitations such as conflicting objectives and limited adaptability.
Approach: They propose an adaptive reasoning framework that applies a uniform, computation-intensive deep reasoning strategy to all problems.
Outcome: The proposed framework reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets.
HiddenDetect: Detecting Jailbreak Attacks against Multimodal Large Language Models via Monitoring Hidden States (2025.acl-long)

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Challenge: Existing studies focus on posthoc alignment techniques, but the underlying safety mechanisms within LVLMs remain unexplored.
Approach: They propose a tuning-free framework that leverages internal activations to enhance safety.
Outcome: The proposed framework outperforms state-of-the-art methods in detecting jailbreak attacks against large vision-language models.
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models (2025.findings-acl)

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Challenge: Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect.
Approach: They propose a method that selects attention heads crucial to the model's prediction as inducing heads and induces hallucinations by dispersing attention of these inducers.
Outcome: The proposed method significantly improves performance on tasks requiring contextual faithfulness, reading comprehension, and question answering.
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions (2020.findings-emnlp)

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Challenge: a novel machine translation-based approach to simplify prescription directions is proposed . the language used by physicians and health professionals includes medical jargon and implicit directives .
Approach: They propose a machine translation-based approach to automatically and reliably simplify prescription directions into patient-friendly language.
Outcome: The proposed system achieves a BLEU score of 60.27 over 530K prescriptions from a large mail-order pharmacy.
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)

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Challenge: Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary.
Approach: They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states.
Outcome: The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates.

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