6 citations · 24 across the 18 of their papers we have counts for
14 papers · 1 filter
Gradually Excavating External Knowledge for Implicit Complex Question Answering
Chang Liu, Xiaoguang Li, Lifeng Shang +4
Recently, large language models (LLMs) have gained much attention for the emergence of human-comparable capabilities and huge potential. However, for open-domain implicit question-…
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
Yuxin Jiang, Bo Huang, Yufei Wang +7
Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pair…
Prompt-Based Length Controlled Generation with Multiple Control Types
Renlong Jie, Xiaojun Meng, Lifeng Shang +2
Large language models (LLMs) have attracted great attention given their strong performance on a wide range of NLP tasks. In practice, users often expect generated texts to fall wit…
Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step
Zezhong Wang, Xingshan Zeng, Weiwen Liu +7
Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phen…
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment
Zhili Liu, Yunhao Gou, Kai Chen +8
As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning…
Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Zhiming Mao, Haoli Bai, Lu Hou +4
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and r…
Learning to Edit: Aligning LLMs with Knowledge Editing
Yuxin Jiang, Yufei Wang, Chuhan Wu +9
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inp…
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang +6
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly fo…
YODA: Teacher-Student Progressive Learning for Language Models
Jianqiao Lu, Wanjun Zhong, Yufei Wang +10
Although large language models (LLMs) have demonstrated adeptness in a range of tasks, they still lag behind human learning efficiency. This disparity is often linked to the inhere…
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
Shijue Huang, Wanjun Zhong, Jianqiao Lu +10
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, part…
Data Management For Training Large Language Models: A Survey
Zige Wang, Wanjun Zhong, Yufei Wang +6
Data plays a fundamental role in training Large Language Models (LLMs). Efficient data management, particularly in formulating a well-suited training dataset, is significant for en…
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Boyang Xue, Weichao Wang, Hongru Wang +7
Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
Yuxin Jiang, Yufei Wang, Xingshan Zeng +7
The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure res…
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
Kai Chen, Chunwei Wang, Kuo Yang +11
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when…