16 citations · 60 across the 39 of their papers we have counts for
8 papers · 2 filters
Multi-Facet Counterfactual Learning for Content Quality Evaluation
Jiasheng Zheng, Hongyu Lin, Boxi Cao +4
Evaluating the quality of documents is essential for filtering valuable content from the current massive amount of information. Conventional approaches typically rely on a single s…
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation
Boxi Cao, Mengjie Ren, Hongyu Lin +4
Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which s…
Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic
Xin Zheng, Jie Lou, Boxi Cao +7
Self-critic has become a crucial mechanism for enhancing the reasoning performance of LLMs. However, current approaches mainly involve basic prompts for intuitive instance-level fe…
Towards Scalable Automated Alignment of LLMs: A Survey
Boxi Cao, Keming Lu, Xinyu Lu +10
Alignment is the most critical step in building large language models (LLMs) that meet human needs. With the rapid development of LLMs gradually surpassing human capabilities, trad…
URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
Zhuoqun Li, Hongyu Lin, Tianshu Wang +7
Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information. Current studies are limited to specific tasks like inf…
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models
Jacek Wiland, Max Ploner, Alan Akbik
Knowledge probing assesses to which degree a language model (LM) has successfully learned relational knowledge during pre-training. Probing is an inexpensive way to compare LMs of…
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
Ruotong Pan, Boxi Cao, Hongyu Lin +5
The rapid development of large language models has led to the widespread adoption of Retrieval-Augmented Generation (RAG), which integrates external knowledge to alleviate knowledg…
Learning or Self-aligning? Rethinking Instruction Fine-tuning
Mengjie Ren, Boxi Cao, Hongyu Lin +6
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and th…