107 citations · 183 across the 26 of their papers we have counts for
11 papers · 1 filter
SAM Decoding: Speculative Decoding via Suffix Automaton
Yuxuan Hu, Ke Wang, Xiaokang Zhang +4
Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have y…
P Law: Scaling Law for Post-Training After Model Pruning
Xiaodong Chen, Yuxuan Hu, Xiaokang Zhang +4
Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs). To recover model performance after pruning, post-training is c…
PCQPR: Proactive Conversational Question Planning with Reflection
Shasha Guo, Lizi Liao, Jing Zhang +2
Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. H…
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Yiyan Li, Haoyang Li, Zhao Pu +6
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance. However, traditional tuning methods often follow a Try-Collect-Adjust a…
A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering
Lingxi Zhang, Jing Zhang, Yanling Wang +2
Large-scale knowledge bases (KBs) like Freebase and Wikidata house millions of structured knowledge. Knowledge Base Question Answering (KBQA) provides a user-friendly way to access…
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Shasha Guo, Lizi Liao, Jing Zhang +3
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the…
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Xinmei Huang, Haoyang Li, Jing Zhang +7
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achie…
Open-World Semi-Supervised Learning for Node Classification
Yanling Wang, Jing Zhang, Lingxi Zhang +5
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-…
Streamlining Redundant Layers to Compress Large Language Models
Xiaodong Chen, Yuxuan Hu, Jing Zhang +3
This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
Large Language Model for Table Processing: A Survey
Weizheng Lu, Jing Zhang, Ju Fan +3
Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table quest…