5 citations · 25 across the 24 of their papers we have counts for
11 papers · 1 filter
Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalization
Yiming Liang, Fang Zhao
Recent years have seen growing interest in applying neural networks and contextualized word embeddings to the parsing of historical languages. However, most advances have focused o…
Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements
Yiming Liang, Yizhi Li, Yantao Du +14
Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predomina…
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
P Team, Xinrun Du, Yifan Yao +94
Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Chenhao Zhang, Xi Feng, Yuelin Bai +18
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…
OmniBench: Towards The Future of Universal Omni-Language Models
Yizhi Li, Yinghao Ma, Ge Zhang +20
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concu…
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Yiming Liang, Ge Zhang, Xingwei Qu +9
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potenti…
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
Ge Zhang, Scott Qu, Jiaheng Liu +42
Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most comp…
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Yuelin Bai, Xinrun Du, Yiming Liang +19
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Xingwei Qu, Yiming Liang, Yucheng Wang +10
Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process…
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
Yizhi LI, Ge Zhang, Xingwei Qu +16
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-fol…
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
Ge Zhang, Xinrun Du, Bei Chen +19
As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger g…