2 citations · 4 across the 13 of their papers we have counts for
14 papers
Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation
Jiaang Li, Zhendong Mao, Quan Wang +2
Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhi…
In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback
Mingye Zhu, Yi Liu, Zheren Fu +2
Training Large Language Models (LLMs) for chain-of-thought reasoning presents a significant challenge: supervised fine-tuning on a single "golden" rationale hurts generalization as…
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
Chiwei Zhu, Benfeng Xu, An Yang +4
Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold c…
Leveraging Robust Optimization for LLM Alignment under Distribution Shifts
Mingye Zhu, Yi Liu, Zheren Fu +2
Preference alignment methods are increasingly critical for steering large language models (LLMs) to generate outputs consistent with human values. While recent approaches often rel…
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization
Mingye Zhu, Yi Liu, Quan Wang +2
Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. However, cur…
ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA
Jiaang Li, Quan Wang, Zhongnan Wang +2
Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors. Most model editing methods are solely designed for…
Feature-Adaptive and Data-Scalable In-Context Learning
Jiahao Li, Quan Wang, Licheng Zhang +2
In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context…
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation
Tianqi Zhong, Zhaoyi Li, Quan Wang +4
Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is…
Benchmarking Large Language Models on Controllable Generation under Diversified Instructions
Yihan Chen, Benfeng Xu, Quan Wang +2
While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constra…
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Fengyi Fu, Lei Zhang, Quan Wang +1
Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empat…
Grammatical Error Correction via Mixed-Grained Weighted Training
Jiahao Li, Quan Wang, Chiwei Zhu +2
The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, b…
On the Calibration of Large Language Models and Alignment
Chiwei Zhu, Benfeng Xu, Quan Wang +2
As large language models attract increasing attention and find widespread application, concurrent challenges of reliability also arise at the same time. Confidence calibration, an…
Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation
Tianqi Zhong, Quan Wang, Jingxuan Han +2
Controllable text generation (CTG) aims to generate text with desired attributes, and decoding-time-based methods have shown promising performance on this task. However, in this pa…
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Jiaang Li, Quan Wang, Yi Liu +2
Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and fac…