https://github.com/MAPLLM/MAPICLR2025sub
공식 코드Improving Planning with Large Language Models: A Modular Agentic Architecture
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StrategyQA
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StrategyQA aims to measure the ability of models to answer questions that require multi-step implicit reasoning.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akirakawabata/reps
공식 코드Rationale-Aware Answer Verification by Pairwise Self-Evaluation
https://github.com/d3mlab/cr-lt-kgqa
공식 코드https://github.com/davidfanzz/scmoe
공식 코드Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
https://github.com/eladsegal/strategyqa
공식 코드Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
https://github.com/githubhyz/delta
공식 코드https://github.com/ink-usc/rationalemultirewarddistillation
공식 코드https://github.com/kumar-shridhar/distiiling-lm
공식 코드Distilling Reasoning Capabilities into Smaller Language Models
https://github.com/lkaesberg/decision-protocols
공식 코드https://github.com/llmpfc/icml24sub
공식 코드Improving Planning with Large Language Models: A Modular Agentic Architecture
https://github.com/miralab-ai/autoreason
공식 코드https://github.com/nardien/kard
공식 코드Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
https://github.com/neuralmind-ai/visconde
공식 코드https://github.com/pphuc25/distil-cd
공식 코드https://github.com/shanka123/llm_pfc
공식 코드Improving Planning with Large Language Models: A Modular Agentic Architecture
https://github.com/timhartill/unseen_questions
공식 코드Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
https://github.com/yiwei98/esc
공식 코드Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
https://github.com/zhentingqi/rstar
공식 코드Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/chrisociepa/allamo
https://github.com/codelion/optillm/blob/main/optillm/rstar.py
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
https://github.com/codelion/optillm/blob/main/optillm/self_consistency.py
Self-Consistency Improves Chain of Thought Reasoning in Language Models
https://github.com/conceptofmind/PaLM-flax
https://github.com/fatebreaker/RARE
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
https://github.com/foundation-model-stack/fms-fsdp
https://github.com/google/paxml
https://github.com/hughbzhang/o1_inference_scaling_laws
Self-Consistency Improves Chain of Thought Reasoning in Language Models
https://github.com/karpathy/llama2.c
https://github.com/lastmile-ai/aiconfig/tree/main/cookbooks/Multi-LLM-Consistency
Self-Consistency Improves Chain of Thought Reasoning in Language Models
https://github.com/lucidrains/CoCa-pytorch
https://github.com/lucidrains/PaLM-jax
https://github.com/lucidrains/PaLM-pytorch
https://github.com/nkluge-correa/teenytinyllama
https://github.com/rvlopes/gloria
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.