5 citations · 8 across the 28 of their papers we have counts for
24 papers · 1 filter
MI-Distillation: Selecting from Model-Interpolated Instruct-Reasoning Data Spectrum for Chain-of-Thought Distillation
Yangsong Lan, Renkai Hu, HongKai Zheng +4
Recent advances in large reasoning models (LRMs) have shown strong performance on complex problems through long chain-of-thought (Long CoT) reasoning. However, distilling such traj…
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models
Yixin Bu, Runze Xia, Guanyun Zou +3
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the mo…
DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting
Hong-Kai Zheng, Piji Li
Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficienc…
CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning
Yangsong Lan, Hongliang Dai, Piji Li
Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency. While prior works attempt to c…
Hallucination Mitigating for Medical Report Generation
Ruoqing Zhao, Runze Xia, Piji Li
In the realm of medical report generation (MRG), the integration of natural language processing has emerged as a vital tool to alleviate the workload of radiologists. Despite the i…
Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation
Guo Chen, Qiuyuan Li, Qiuxian Li +3
In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify pote…
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scar…
Generating Diverse Training Samples for Relation Extraction with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investig…
ReFreeKV: Towards Threshold-Free KV Cache Compression
Xuanfan Ni, Liyan Xu, Chenyang Lyu +6
To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can accomplish lossless memory reduction on…
MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents
Congchi Yin, Feng Li, Shu Zhang +5
The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is pro…
LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models
Renzhi Wang, Piji Li
Large language models (LLMs) require continual knowledge updates to stay abreast of the ever-changing world facts, prompting the formulation of lifelong model editing task. While r…
MEMoE: Enhancing Model Editing with Mixture of Experts Adaptors
Renzhi Wang, Piji Li
Model editing aims to efficiently alter the behavior of Large Language Models (LLMs) within a desired scope, while ensuring no adverse impact on other inputs. Recent years have wit…
Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning
Renzhi Wang, Piji Li
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of Large Language Models (LLMs) to various downstream applications. However, the effectiveness of the PEF…
5W1H Extraction With Large Language Models
Yang Cao, Yangsong Lan, Feiyan Zhai +1
The extraction of essential news elements through the 5W1H framework (\textit{What}, \textit{When}, \textit{Where}, \textit{Why}, \textit{Who}, and \textit{How}) is critical for ev…
A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks
Xuanfan Ni, Piji Li
Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledg…
Language Reconstruction with Brain Predictive Coding from fMRI Data
Congchi Yin, Ziyi Ye, Piji Li
Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. However, there is a lack of ne…
XLBench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies
Xuanfan Ni, Hengyi Cai, Xiaochi Wei +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks but are constrained by their small context window sizes. Various efforts have been propos…
Characteristic AI Agents via Large Language Models
Xi Wang, Hongliang Dai, Shen Gao +1
The advancement of Large Language Models (LLMs) has led to significant enhancements in the performance of chatbot systems. Many researchers have dedicated their efforts to the deve…
An Empirical Investigation of Domain Adaptation Ability for Chinese Spelling Check Models
Xi Wang, Ruoqing Zhao, Hongliang Dai +1
Chinese Spelling Check (CSC) is a meaningful task in the area of Natural Language Processing (NLP) which aims at detecting spelling errors in Chinese texts and then correcting thes…
Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning
Ruoqing Zhao, Xi Wang, Hongliang Dai +2
Automated radiology report generation has the potential to improve radiology reporting and alleviate the workload of radiologists. However, the medical report generation task poses…
Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding
Congchi Yin, Qian Yu, Zhiwei Fang +2
Recent major milestones have successfully reconstructed natural language from non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram…
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation
Renzhi Wang, Jing Li, Piji Li
Diffusion models have garnered considerable interest in the field of text generation. Several studies have explored text diffusion models with different structures and applied them…
Topic-Guided Self-Introduction Generation for Social Media Users
Chunpu Xu, Jing Li, Piji Li +1
Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction…
CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation
Congchi Yin, Piji Li, Zhaochun Ren
Dialogue structure discovery is essential in dialogue generation. Well-structured topic flow can leverage background information and predict future topics to help generate controll…