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ARC

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tagoyal/dae-factuality

공식 코드

Evaluating Factuality in Generation with Dependency-level Entailment

https://github.com/tanchongmin/arc-challenge

공식 코드

An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge

https://github.com/tensorflow/mesh

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ST-MoE: Designing Stable and Transferable Sparse Expert Models

https://github.com/thunlp-mt/symbol2language

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Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models

https://github.com/timhartill/unseen_questions

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Teaching Smaller Language Models To Generalise To Unseen Compositional Questions

https://github.com/tzshi/dp-parser-emnlp17

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Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

https://github.com/uiuc-arc/itergen

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IterGen: Iterative Semantic-aware Structured LLM Generation with Backtracking

https://github.com/uiuc-arc/llm-code-watermark

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Is The Watermarking Of LLM-Generated Code Robust?

https://github.com/vcg/ffd4denspineem

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Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures

https://github.com/victorvikram/conceptarc

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The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain

https://github.com/vigna/monotonicity

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Monotonicity in Undirected Networks

https://github.com/vrombouts/Generic-checker-for-CP-Solver-s-constraints

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Testing Global Constraints

https://github.com/wwxu21/AMR-SG

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Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

https://github.com/wwxu21/cgr

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Exploiting Reasoning Chains for Multi-hop Science Question Answering

https://github.com/xnhyacinth/nesycd

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Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks

https://github.com/xu3kev/barc

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Combining Induction and Transduction for Abstract Reasoning

https://github.com/xxares/todo

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TODO: Enhancing LLM Alignment with Ternary Preferences

https://github.com/yahoo/YaraParser

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Yara Parser: A Fast and Accurate Dependency Parser

https://github.com/yixinliu233/arc

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ARC: A Generalist Graph Anomaly Detector with In-Context Learning

https://github.com/you68681/gpar

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Generalized Planning for the Abstraction and Reasoning Corpus

https://github.com/yu-fangxu/for

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Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal Examples

https://github.com/yuwen-chen616/PHT-CAD

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PHT-CAD: Efficient CAD Parametric Primitive Analysis with Progressive Hierarchical Tuning

https://github.com/yzhangcs/crfpar

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Efficient Second-Order TreeCRF for Neural Dependency Parsing

https://github.com/zhangq327/arc

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Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap

https://github.com/zhaoyd1/dep_transformer_grammars

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Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models

https://github.com/zhuchen03/FreeLB

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FreeLB: Enhanced Adversarial Training for Natural Language Understanding

https://github.com/zodiark-ch/mater-of-our-emnlp2023-paper

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How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning

https://github.com/MS-P3/code6/tree/main/finetune

Finetuned Language Models Are Zero-Shot Learners

https://github.com/ZHO9504/Select-Key-Terms-in-a-Question

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

https://github.com/alejandrodemiquel/arc

On the Measure of Intelligence

https://github.com/amazon-science/controllable-readability-summarization

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

https://github.com/amazon-science/fact-graph

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

https://github.com/arcprize/arc-agi-2

On the Measure of Intelligence

https://github.com/artyompal/kaggle-abstract-reasoning

On the Measure of Intelligence

https://github.com/axiomlab/Cable

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

https://github.com/bigcode-project/starcoder

Finetuned Language Models Are Zero-Shot Learners

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