https://github.com/longyuewangdcu/disco-bench
공식 코드Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text
Representing visual classification as a linear combination of words
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer
Trained on 100 million words and still in shape: BERT meets British National Corpus
More Room for Language: Investigating the Effect of Retrieval on Language Models
WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words
You Know What I'm Saying: Jailbreak Attack via Implicit Reference
CoVR-2: Automatic Data Construction for Composed Video Retrieval
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search
Discovering Preference Optimization Algorithms with and for Large Language Models
Think Before You Act: Decision Transformers with Working Memory
Accommodating Audio Modality in CLIP for Multimodal Processing
Code Soliloquies for Accurate Calculations in Large Language Models
Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.