https://github.com/namin/llm-verified-with-monte-carlo-tree-search
공식 코드VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search
Multi-Task Deep Neural Networks for Natural Language Understanding
Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models
Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
Human-centric Dialog Training via Offline Reinforcement Learning
DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
LongProLIP: A Probabilistic Vision-Language Model with Long Context Text
Rethinking the Role of Proxy Rewards in Language Model Alignment
TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
Can discrete information extraction prompts generalize across language models?
Enhancing Biomedical Relation Extraction with Directionality
Matching Patients to Clinical Trials with Large Language Models
Visually Dehallucinative Instruction Generation: Know What You Don't Know
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.