23 citations · 40 across the 18 of their papers we have counts for
14 papers · 1 filter
BitNet Text Embeddings
Zhen Li, Xin Huang, Liang Wang +8
LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding i…
Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
Di Zhang, Xun Wu, Shaohan Huang +9
Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they ha…
11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
Chengzu Li, Wenshan Wu, Huanyu Zhang +6
For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Chengzu Li, Wenshan Wu, Huanyu Zhang +5
Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it s…
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
Jinheng Wang, Hansong Zhou, Ting Song +5
Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, present a promising approach to enhancing the efficiency of LLMs in terms of speed and energ…
CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays
Nuowei Liu, Xinhao Chen, Hongyi Wu +6
Existing rhetorical understanding and generation datasets or corpora primarily focus on single coarse-grained categories or fine-grained categories, neglecting the common interrela…
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning
Yadong Zhang, Shaoguang Mao, Wenshan Wu +4
This paper introduces BI-Directional DEliberation Reasoning (BIDDER), a novel reasoning approach to enhance the decision rationality of language models. Traditional reasoning metho…
Meta Reasoning for Large Language Models
Peizhong Gao, Ao Xie, Shaoguang Mao +4
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) inspired by human meta-reasoning. Traditional in-context…
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Wenshan Wu, Shaoguang Mao, Yadong Zhang +4
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial a…
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
Yadong Zhang, Shaoguang Mao, Tao Ge +7
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that n…
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
Yadong Zhang, Shaoguang Mao, Tao Ge +4
Strategic reasoning is a complex yet essential capability for intelligent agents. It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent…
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
Shaoguang Mao, Yuzhe Cai, Yan Xia +5
This paper introduces Alympics (Olympics for Agents), a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a ver…
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation
Wang You, Wenshan Wu, Yaobo Liang +8
Plan-and-Write is a common hierarchical approach in long-form narrative text generation, which first creates a plan to guide the narrative writing. Following this approach, several…
Low-code LLM: Graphical User Interface over Large Language Models
Yuzhe Cai, Shaoguang Mao, Wenshan Wu +10
Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a nov…