9 papers
TopoChunker: Topology-Aware Agentic Document Chunking Framework
Xiaoyu Liu
Current document chunking methods for Retrieval-Augmented Generation (RAG) typically linearize text. This forced linearization strips away intrinsic topological hierarchies, creati…
DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning
Xiaoyu Liu, Xiaoyu Guan, Di Liang +1
Supervised fine-tuning (SFT) is a crucial step for adapting large language models (LLMs) to downstream tasks. However, conflicting objectives across heterogeneous SFT tasks often i…
PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback
Dehao Yuan, Tyler Farnan, Stefan Tesliuc +8
Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly…
DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models
Adarsh Kumarappan, Pareesa Ameneh Golnari, Wen Wen +5
DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,800 evaluation instances across six pro…
Predicate-Argument Structure Divergences in Chinese and English Parallel Sentences and their Impact on Language Transfer
Rocco Tripodi, Xiaoyu Liu
Cross-lingual Natural Language Processing (NLP) has gained significant traction in recent years, offering practical solutions in low-resource settings by transferring linguistic kn…
Uncovering Pretraining Code in LLMs: A Syntax-Aware Attribution Approach
Yuanheng Li, Zhuoyang Chen, Xiaoyun Liu +5
As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training data have grown, especially in…
Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling
Xiaoyu Liu, Di Liang, Chang Dai +9
Reward Models (RMs) are key components for evaluating and guiding language model outputs. However, traditional scalar RMs often struggle with incorporating contextual and backgroun…
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Benjamin Hsu, Xiaoyu Liu, Huayang Li +6
Document translation poses a challenge for Neural Machine Translation (NMT) systems. Most document-level NMT systems rely on meticulously curated sentence-level parallel data, assu…
A Framework for Effective Invocation Methods of Various LLM Services
Can Wang, Dianbo Sui, Bolin Zhang +4
Large Language Models (LLMs) have shown impressive abilities in solving various natural language processing tasks and are now widely offered as services. LLM services enable users…