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20232026
most citedR-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models

5 citations · 15 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

SurveyReview: A Reviewer-Aligned Benchmark for Survey Evaluators

Yuheng Zhang, Yuanchun Wang, Fanjin Zhang +4

The rapid advancement of large language models has transformed survey writing from a months-long manual effort into an automated process. As generation scales, reliable evaluation…

cs.CL2026

RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension

Yelin Chen, Fanjin Zhang, Suping Sun +8

Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables, yet existing benchmarks offer limite…

cs.CL2025

LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning

Joy Lim Jia Yin, Daniel Zhang-Li, Jifan Yu +8

Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators f…

cs.CL2024★ 5 cited

R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models

Shangqing Tu, Yuanchun Wang, Jifan Yu +6

Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Langu…

cs.CL2024★ 4 cited

SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking

Yuanchun Wang, Jifan Yu, Zijun Yao +13

Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods str…

cs.CL2023★ 1 cited

GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model

Shicheng Tan, Weng Lam Tam, Yuanchun Wang +9

Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment…

cs.CL2023

Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method

Shicheng Tan, Weng Lam Tam, Yuanchun Wang +4

The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly…