53 citations · 282 across the 117 of their papers we have counts for
24 papers · 2 filters
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model
Jiawei Chen, Wentao Chen, Jing Su +6
Large language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not…
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with k…
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…
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
Zichao Li, Shaojie He, Meng Liao +6
Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short…
Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models
Hao Xiang, Bowen Yu, Hongyu Lin +7
The key to effective alignment lies in high-quality preference data. Recent research has focused on automated alignment, which involves developing alignment systems with minimal hu…
READoc: A Unified Benchmark for Realistic Document Structured Extraction
Zichao Li, Aizier Abulaiti, Yaojie Lu +5
Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequa…
REInstruct: Building Instruction Data from Unlabeled Corpus
Shu Chen, Xinyan Guan, Yaojie Lu +3
Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling…
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…
Open Grounded Planning: Challenges and Benchmark Construction
Shiguang Guo, Ziliang Deng, Hongyu Lin +3
The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on lev…
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation
Xueru Wen, Jie Lou, Xinyu Lu +8
Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, pr…
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…
Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching
Tianshu Wang, Xiaoyang Chen, Hongyu Lin +5
Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-bas…
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…
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Jiawei Chen, Hongyu Lin, Xianpei Han +4
Few-shot NER aims to identify entities of target types with only limited number of illustrative instances. Unfortunately, few-shot NER is severely challenged by the intrinsic preci…
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models
Zhuoqun Li, Hongyu Lin, Yaojie Lu +3
Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs. However,…
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
Xin Zheng, Qiming Zhu, Hongyu Lin +3
Executing computer programs described in natural language has long been a pursuit of computer science. With the advent of enhanced natural language understanding capabilities exhib…
Academically intelligent LLMs are not necessarily socially intelligent
Ruoxi Xu, Hongyu Lin, Xianpei Han +2
The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear. Inspired by estab…
SoFA: Shielded On-the-fly Alignment via Priority Rule Following
Xinyu Lu, Bowen Yu, Yaojie Lu +5
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values. This requirement challenges existing alignment methods due to di…
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…
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Ning Bian, Xianpei Han, Hongyu Lin +3
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning…
AI for social science and social science of AI: A Survey
Ruoxi Xu, Yingfei Sun, Mengjie Ren +5
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possib…