activity
20182026
most citedRAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

9 citations · 9 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

ATLAS: All-round Testing of Long-context Abilities across Scales

Deli Huang, Cunguang Wang, Hongyin Tang +15

Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure m…

cs.CL2026

AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations

Cheng Jiayang, Dongyu Ru, Lin Qiu +4

Long-horizon interactions between users and LLM-based assistants necessitate effective memory management, yet current approaches face challenges in training and evaluation of memor…

cs.CL20249 cited

RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Dongyu Ru, Lin Qiu, Xiangkun Hu +15

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…

cs.CL2024

RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models

Xiangkun Hu, Dongyu Ru, Lin Qiu +7

Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…

cs.CL2023

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

Cheng Jiayang, Lin Qiu, Tsz Ho Chan +9

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind larg…

cs.CL2020

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space

Dongyu Ru, Jiangtao Feng, Lin Qiu +5

Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typic…