https://github.com/amzn/user-satisfaction-modeling
공식 코드Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts
LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset
The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings
MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
Detecting out-of-distribution text using topological features of transformer-based language models
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Evaluating Large Language Model Biases in Persona-Steered Generation
Training and Inference on Any-Order Autoregressive Models the Right Way
Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification
Revisiting Few-Shot Object Detection with Vision-Language Models
Garbage in, garbage out: Zero-shot detection of crime using Large Language Models
CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection
ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIF
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
Distributional Discrepancy: A Metric for Unconditional Text Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.