10 citations · 23 across the 9 of their papers we have counts for
10 papers
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…
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…
FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts
Xinyi Wang, Lirong Gao, Haobo Wang +2
Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a widely adopted strategy for adapting pre-trained Large Language Models (LLMs) to downstream tasks, significantly re…
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…
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…
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…
Revisiting the Knowledge Injection Frameworks
Peng Fu, Yiming Zhang, Haobo Wang +2
In recent years, large language models (LLMs), such as GPTs, have attained great impact worldwide. However, how to adapt these LLMs to better suit the vertical domain-specific task…
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Liangyu Zha, Junlin Zhou, Liyao Li +22
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made…
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Hao Chen, Yiming Zhang, Qi Zhang +5
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instru…
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
Yiming Zhang, Min Zhang, Sai Wu +1
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence. The develop…