30 citations · 47 across the 24 of their papers we have counts for
21 papers · 1 filter
Neuron-Guided Fine-Tuning: Unlocking Efficient Alignment Mechanisms for Large Language Models
Zeyu Wu, Junchao Wu, Shudong Liu +8
Existing Supervised Fine-Tuning paradigms, particularly Full Parameter Fine-Tuning are often plagued by parameter redundancy, inconsistent data quality, and catastrophic forgetting…
SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
Haonan He, Haodi Lei, Yun Luo +13
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-co…
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Fengying Ye, Yanming Sun, Runzhe Zhan +3
Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We present G-IdiomAlign, a…
Neuron-Aware Data Selection In Instruction Tuning For Large Language Models
Xin Chen, Junchao Wu, Shu Yang +6
Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). Recent studies indicate that excessive IT d…
Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
Yanming Sun, Runzhe Zhan, Chi Seng Cheang +7
\textbf{RE}trieval-\textbf{A}ugmented \textbf{L}LM-based \textbf{M}achine \textbf{T}ranslation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but…
Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
Runzhe Zhan, Zhihong Huang, Xinyi Yang +3
Recent advancements in large reasoning models (LRMs) have introduced an intermediate "thinking" process prior to generating final answers, improving their reasoning capabilities on…
Path Drift in Large Reasoning Models:How First-Person Commitments Override Safety
Yuyi Huang, Runzhe Zhan, Lidia S. Chao +2
As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a key paradigm for structured infer…
Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning
Zhilin Wang, Zhe Yang, Yun Luo +8
Enhancing the ability of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to interpret sheet music is a crucial step toward building AI musicians. However,…
RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang +7
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well,…
Rethinking Prompt-based Debiasing in Large Language Models
Xinyi Yang, Runzhe Zhan, Derek F. Wong +3
Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on th…
More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
Evan Chen, Run-Jun Zhan, Yan-Bai Lin +1
Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduc…
Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models
Yuyi Huang, Runzhe Zhan, Derek F. Wong +2
Large language models (LLMs) have significantly influenced various industries but suffer from a critical flaw, the potential sensitivity of generating harmful content, which poses…
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
Junchao Wu, Runzhe Zhan, Derek F. Wong +4
Detecting text generated by large language models (LLMs) is of great recent interest. With zero-shot methods like DetectGPT, detection capabilities have reached impressive levels.…
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
Junchao Wu, Runzhe Zhan, Derek F. Wong +4
The efficacy of detectors for texts generated by large language models (LLMs) substantially depends on the availability of large-scale training data. However, white-box zero-shot d…
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model
Runzhe Zhan, Xinyi Yang, Derek F. Wong +2
While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been…
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
Haoyun Xu, Runzhe Zhan, Derek F. Wong +1
Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed t…
Human-in-the-loop Machine Translation with Large Language Model
Xinyi Yang, Runzhe Zhan, Derek F. Wong +2
The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities. The research community has conducted several…
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Junchao Wu, Shu Yang, Runzhe Zhan +3
The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at…
Variance-Aware Machine Translation Test Sets
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
We release 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT…
Difficulty-Aware Machine Translation Evaluation
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attentio…
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the transl…