https://github.com/uclanlp/socialbias-dataset-construction-biases
공식 코드The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Learning Python Code Suggestion with a Sparse Pointer Network
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case
A Study of Generative Large Language Model for Medical Research and Healthcare
ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution
ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5
Making Fast Graph-based Algorithms with Graph Metric Embeddings
Adapting a Language Model While Preserving its General Knowledge
Induced Model Matching: How Restricted Models Can Help Larger Ones
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
Contextual information integration for stance detection via cross-attention
Protecting multimodal large language models against misleading visualizations
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Differentially Private Steering for Large Language Model Alignment
Are Emergent Abilities in Large Language Models just In-Context Learning?
Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions
Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.