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20242026
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cs.CL2026

Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination

Lirong Gao, Zeqing Wang, Yuyan Cai +6

While Large Language Models (LLMs) have increasingly assisted in historical tasks such as text processing, their capacity for professional-level historical reasoning remains undere…

cs.CL2026

Table as a Modality for Large Language Models

Liyao Li, Chao Ye, Wentao Ye +9

To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed…

cs.CL2024

FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents

Yilun Zhao, Yitao Long, Yuru Jiang +7

We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyz…

cs.CL2024

DORY: Deliberative Prompt Recovery for LLM

Lirong Gao, Ru Peng, Yiming Zhang +1

Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc. The trend towards inference-on…

cs.CL2024

RECOST: External Knowledge Guided Data-efficient Instruction Tuning

Qi Zhang, Yiming Zhang, Haobo Wang +1

In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficie…