大模型推理能力

Human-Centric Autonomous Systems With LLMs for User Command Reasoning

Yi Yang, Qingwen Zhang, Ci Li, Daniel Simões Marta, Nazre Batool, John Folkesson

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024

On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs

Pei-Chi Lo, Yi-Hang Tsai, Ee-Peng Lim, San-Yih Hwang

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments Presented at the Generative-IR Workshop during SIGIR 2023. https://coda.io/@sigir/gen-ir

DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

Mohammadreza Pourreza, Davood Rafiei

专题命中 复杂问题求解 ：self-correction(title)；reasoning(abstract)；分类 cs.CL、cs.AI

Comments To appear in NeurIPS 2023

DOMINO: A Dual-System for Multi-step Visual Language Reasoning

Peifang Wang, Olga Golovneva, Armen Aghajanyan, Xiang Ren, Muhao Chen, Asli Celikyilmaz, Maryam Fazel-Zarandi

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.LG

Neural Amortized Inference for Nested Multi-agent Reasoning

Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, Tianmin Shu

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

Comments 8 pages, 10 figures

Beyond Classification: Financial Reasoning in State-of-the-Art Language Models

Guijin Son, Hanearl Jung, Moonjeong Hahm, Keonju Na, Sol Jin

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted by FinNLP (Financial Technology and Natural Language Processing) @ IJCAI2023 as long paper

DePlot: One-shot visual language reasoning by plot-to-table translation

Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments ACL 2023 (Findings)

Fact-Checking Complex Claims with Program-Guided Reasoning

Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments ACL 2023 (main conference, long paper)

Case-Based Reasoning with Language Models for Classification of Logical Fallacies

Zhivar Sourati, Filip Ilievski, Hông-Ân Sandlin, Alain Mermoud

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Jiawei Zhang

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

Comments 34 pages, 3 figures, 8 tables

Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning

Zhengzhong Liang, Zeyu Zhang, Steven Bethard, Mihai Surdeanu

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes

Justin Reppert, Ben Rachbach, Charlie George, Luke Stebbing, Jungwon Byun, Maggie Appleton, Andreas Stuhlmüller

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Visual Programming: Compositional visual reasoning without training

Tanmay Gupta, Aniruddha Kembhavi

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted at EMNLP'22

Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models

Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments 12 pages, NAACL-Findings-2022

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

Comments ICLR 2022. Project page: https://comphyreasoning.github.io/

JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering

Yueqing Sun, Qi Shi, Le Qi, Yu Zhang

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted by NAACL 2022 main conference (Long paper)

Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering

Mingxiao Li, Marie-Francine Moens

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Xiaobai Ma, David Isele, Jayesh K. Gupta, Kikuo Fujimura, Mykel J. Kochenderfer

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

Comments AAAI 2022

GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.LG

Comments Published at ICLR 2022. All code, data, and pretrained models are available at https://github.com/snap-stanford/GreaseLM

Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance

Karthikeyan K, Aalok Sathe, Somak Aditya, Monojit Choudhury

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments Workshop on Multilingual Representation Learning (MRL 2021), at Empirical Methods in Natural Language Processing (EMNLP 2021)

CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning

Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, Ashish Kapoor

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning

Dandan Song, Siyi Ma, Zhanchen Sun, Sicheng Yang, Lejian Liao

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted to 37th International Conference on Machine Learning (ICML 2020), Extended version with proofs and additional experimental details and results, 27 pages

Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

Jaap Jumelet, Willem Zuidema, Dieuwke Hupkes

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments To appear at CoNLL2019

Reasoning-Driven Question-Answering for Natural Language Understanding

Daniel Khashabi

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments PhD Dissertation; Presented to Computer and Information Sciences department, at the University of Pennsylvania

CLEAR: A Dataset for Compositional Language and Elementary Acoustic Reasoning

Jerome Abdelnour, Giampiero Salvi, Jean Rouat

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.LG

Comments NeurIPS 2018 Visually Grounded Interaction and Language (ViGIL) Workshop

Compositional Language Understanding with Text-based Relational Reasoning

Koustuv Sinha, Shagun Sodhani, William L. Hamilton, Joelle Pineau

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Comments 4 pages of main content, to be presented at Relational Representation Learning Workshop, NIPS 2018, Montreal

Pruning via Causal Attribution Preserves Reasoning Performance in Large Language Models

基于因果归因的剪枝保留大型语言模型的推理性能

Amogh Sheth, Biruk Assefa, Yi Wen Huang, Andrew Lin, Yuhao Ge

机构 * Edison Academy Magnet School（爱迪生学院磁石学校） ； Massachusetts Institute of Technology（麻省理工学院） ； State University of New York College at Plattsburgh（纽约州立大学普拉茨堡学院） ； The University of Texas at Austin（德克萨斯大学奥斯汀分校） ； Independent Researcher（独立研究员） 纠错

专题命中 复杂问题求解 ：reasoning(title,abstract)；分类 cs.CL

AI总结 提出无需训练的因果归因剪枝（CAP）方法，通过测量注意力头对推理任务的因果影响进行细粒度剪枝，在20%稀疏度下相比Wanda在ARC-Challenge上准确率提升高达61%。

Comments Accepted at the ICLR 2026 Workshop on LLM Reasoning. 13 pages, 2 figures

Decomposing LLM Self-Correction: The Accuracy-Correction Paradox and Error Depth Hypothesis

分解大语言模型的自我纠正：准确性-纠正悖论与错误深度假说

Yin Li

机构 * University of Birmingham（伯明翰大学） 纠错

专题命中 复杂问题求解 ：self-correction(title,abstract)；分类 cs.AI

AI总结 本研究揭示大语言模型在自我纠正中的准确性-纠正悖论，提出错误深度假说，发现更强模型犯更深入的错误，且错误检测与纠正成功率无直接关联。

Comments 9 pages, 2 figures, 3 tables. Code available at https://github.com/Kevin0304-li/llm-self-correction