activity
20222025
most citedLow-code LLM: Graphical User Interface over Large Language Models

23 citations · 49 across the 18 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CL2024★ 1 cited

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark

Qihao Zhao, Yangyu Huang, Tengchao Lv +8

Multiple-choice question (MCQ) datasets like Massive Multitask Language Understanding (MMLU) are widely used to evaluate the commonsense, understanding, and problem-solving abiliti…

cs.CL2024

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…

cs.CL2024★ 2 cited

Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks

Fangru Lin, Shaoguang Mao, Emanuele La Malfa +7

Language is not monolithic. While benchmarks, including those designed for multiple languages, are often used as proxies to evaluate the performance of Large Language Models (LLMs)…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 2 cited

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…

cs.CL2024★ 2 cited

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…

cs.CL2024★ 6 cited

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…

cs.CL2024★ 1 cited

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…