8 citations · 21 across the 18 of their papers we have counts for
18 papers
ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs
Feng He, Zijun Chen, Xinnian Liang +4
Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought (Long CoT) reasoning have demonstrated remarkable cross-domain generalization capabilities. Howe…
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning
Chenwei Lou, Zewei Sun, Xinnian Liang +6
Large Language Models (LLMs) have demonstrated remarkable capabilities but often face challenges with tasks requiring sophisticated reasoning. While Chain-of-Thought (CoT) promptin…
What Are They Talking About? A Benchmark of Knowledge-Grounded Discussion Summarization
Weixiao Zhou, Junnan Zhu, Gengyao Li +4
Traditional dialogue summarization primarily focuses on dialogue content, assuming it comprises adequate information for a clear summary. However, this assumption often fails for d…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
Jian Yang, Hongcheng Guo, Yuwei Yin +7
Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between…
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Bing Wang, Changyu Ren, Jian Yang +8
Recent LLM-based Text-to-SQL methods usually suffer from significant performance degradation on "huge" databases and complex user questions that require multi-step reasoning. Moreo…
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Weixiao Zhou, Gengyao Li, Xianfu Cheng +4
Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a…
KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
Jiaqi Bai, Zhao Yan, Jian Yang +3
Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. They require a large knowledge base and a strong knowledge retriev…
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking
Jiaqi Bai, Hongcheng Guo, Jiaheng Liu +4
Retrieval-enhanced text generation has shown remarkable progress on knowledge-intensive language tasks, such as open-domain question answering and knowledge-enhanced dialogue gener…
Retrieval-Augmented Classification with Decoupled Representation
Xinnian Liang, Shuangzhi Wu, Hui Huang +3
Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is…
SCM: Enhancing Large Language Model with Self-Controlled Memory Framework
Bing Wang, Xinnian Liang, Jian Yang +6
Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. To address this limitation, in…
Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models
Xinnian Liang, Zefan Zhou, Hui Huang +5
Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and complete…
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality
Xinnian Liang, Shuangzhi Wu, Chenhao Cui +3
Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialo…
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
Xinnian Liang, Jing Li, Shuangzhi Wu +4
Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider ef…
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
Chenhao Cui, Xinnian Liang, Shuangzhi Wu +1
Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features, then these features are fu…
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
Xinnian Liang, Shuangzhi Wu, Mu Li +1
Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) ha…
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
Xinnian Liang, Shuangzhi Wu, Mu Li +1
Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and docu…
StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Ze Yang, Wei Wu, Can Xu +5
Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for trainin…