1 citations · 2 across the 7 of their papers we have counts for
9 papers · 1 filter
On LLM-Based Scientific Inductive Reasoning Beyond Equations
Brian S. Lin, Jiaxin Yuan, Zihan Zhou +8
As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited e…
Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification
Fernando Gabriela García, Qiyang Shi, Zilin Feng
This research introduces VeriFact-CoT (Verified Factual Chain-of-Thought), a novel method designed to address the pervasive issues of hallucination and the absence of credible cita…
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization
Zhensheng Jin, Xinze Li, Yifan Ji +7
Recent advances in Chain-of-Thought (CoT) prompting have substantially improved the reasoning capabilities of Large Language Models (LLMs). However, these methods often suffer from…
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation
Hao Chen, Yukun Yan, Sen Mei +9
Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the…
ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
Xuanle Zhao, Xuexin Liu, Haoyue Yang +5
Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to…
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Jianling Li, Shangzhan Li, Zhenye Gao +9
Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and access…