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Liang Chen

8 papers hereh-index 587 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author1

Across the 6 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI2
  • cs.DC1
  • cs.SE1
same name
  • Liang Chen — 25 papers, h 9
  • Liang Chen — 16 papers, h 8
  • Liang Chen — 16 papers
  • Liang Chen — 13 papers, h 28
  • Liang Chen — 11 papers, h 11
  • Liang Chen — 8 papers, h 16

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedInference Optimization of Foundation Models on AI Accelerators

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation

Wenzhen Luo, Wei Guan, Yifan Yao +6

We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 database…

cs.CL2024

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine

Xiaochen Wang, Junqing He, Liang Chen +5

Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answer…

cs.CL2024

Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models

Xiao Peng, Liang Chen

Recently, large language models (LLMs) like ChatGPT, LLaMA, and Claude have prevailed in countless domains, including legal scenarios. With LLMs' rapid technological progress, the…

cs.CL2023

Meta Semantic Template for Evaluation of Large Language Models

Yachuan Liu, Liang Chen, Jindong Wang +2

Do large language models (LLMs) genuinely understand the semantics of the language, or just memorize the training data? The recent concern on potential data contamination of LLMs h…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.