718 citations · 1k across the 44 of their papers we have counts for
15 papers · 1 filter
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
Yun Luo, Yingjie Li, Xiangkun Hu +5
As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues. To combat this issue…
Can Language Models Learn to Skip Steps?
Tengxiao Liu, Qipeng Guo, Xiangkun Hu +4
Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intrig…
ReAttention: Training-Free Infinite Context with Finite Attention Scope
Xiaoran Liu, Ruixiao Li, Qipeng Guo +7
The long-context capability of the Large Language Models (LLM) has made significant breakthroughs, but the maximum supported context length in length extrapolation remains a critic…
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models
Qingkai Min, Qipeng Guo, Xiangkun Hu +3
Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utili…
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
Xiangkun Hu, Dongyu Ru, Lin Qiu +7
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Zhangyue Yin, Qiushi Sun, Qipeng Guo +9
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances…
InternLM2 Technical Report
Zheng Cai, Maosong Cao, Haojiong Chen +97
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advan…
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
Yuhong Sun, Zhangyue Yin, Qipeng Guo +3
Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous…
NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
Cunxiang Wang, Ruoxi Ning, Boqi Pan +8
Recent advancements in Large Language Models (LLMs) have pushed the boundaries of natural language processing, especially in long-context understanding. However, the evaluation of…
LongWanjuan: Towards Systematic Measurement for Long Text Quality
Kai Lv, Xiaoran Liu, Qipeng Guo +4
The quality of training data are crucial for enhancing the long-text capabilities of foundation models. Despite existing efforts to refine data quality through heuristic rules and…
Turn Waste into Worth: Rectifying Top- Router of MoE
Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei +7
Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top- routing mechanism su…
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
Demin Song, Honglin Guo, Yunhua Zhou +8
The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural…
Data-free Weight Compress and Denoise for Large Language Models
Runyu Peng, Yunhua Zhou, Qipeng Guo +4
Large Language Models (LLMs) are reshaping the research landscape in artificial intelligence, particularly as model parameters scale up significantly, unlocking remarkable capabili…
Identifying Semantic Induction Heads to Understand In-Context Learning
Jie Ren, Qipeng Guo, Hang Yan +4
Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness. To ga…
F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods
Yu Sun, Keyu Chen, Shujie Wang +6
Large language models (LLMs) garner significant attention for their unprecedented performance, leading to an increasing number of researches evaluating LLMs. However, these evaluat…