https://github.com/rymarinelli/leaking_lora
공식 코드Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 7
- 보존된 평가 기록
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Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ryuichi-sumida/lufy
공식 코드https://github.com/ryuryukke/HowYouPromptMatters
공식 코드https://github.com/ryuryukke/OUTFOX
공식 코드https://github.com/ryutaro-a/sumrec
공식 코드SumRec: A Framework for Recommendation using Open-Domain Dialogue
https://github.com/s-nlp/konstruktor
공식 코드Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
https://github.com/s-nlp/s3
공식 코드S3: A Simple Strong Sample-effective Multimodal Dialog System
https://github.com/s-vigneshwaran/revisiting-the-classics
공식 코드https://github.com/s222416822/llmqfl
공식 코드LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
https://github.com/sa4s-serc/llm4adr
공식 코드https://github.com/sa5r/kgrp
공식 코드Knowledge Graph Completion using Structural and Textual Embeddings
https://github.com/saccharomycetes/text-based-traffic
공식 코드Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
https://github.com/saccharomycetes/text-based-traffic-understanding
공식 코드https://github.com/sai990323/tallrec
공식 코드https://github.com/saifkhichi96/situational-llm
공식 코드SituationalLLM: Proactive language models with scene awareness for dynamic, contextual task guidance
https://github.com/sail-sg/agent-smith
공식 코드Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
https://github.com/sail-sg/regmix
공식 코드RegMix: Data Mixture as Regression for Language Model Pre-training
https://github.com/sajqavril/Code-Repo-for-R-GCN-Auto-Encoder
공식 코드Type Information-Assisted Self-Supervised Knowledge Graph Denoising
https://github.com/sakanaai/edinet-bench
공식 코드EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
https://github.com/sakanaai/edinet2dataset
공식 코드EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
https://github.com/sakura-not-sleep/ca_fvd
공식 코드Consistency-aware Fake Videos Detection on Short Video Platforms
https://github.com/salanueva/CBM
공식 코드Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
https://github.com/salehiac/languagegroundedqd
공식 코드Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
https://github.com/salesforce/CodeGen
공식 코드CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/salesforce/MultiHopKG
공식 코드https://github.com/salesforce/diversesumm
공식 코드https://github.com/salesforce/jaxformer
공식 코드CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/salesforce/summary-of-a-haystack
공식 코드Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems
https://github.com/salesforceairesearch/agentlite
공식 코드AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
https://github.com/salesforceairesearch/answer-engine-eval
공식 코드Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses
https://github.com/salesforceairesearch/contextualjudgebench
공식 코드Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings
https://github.com/salesforceairesearch/faitheval
공식 코드https://github.com/salesforceairesearch/jetts-benchmark
공식 코드https://github.com/sallmanm/synsim
공식 코드https://github.com/salt-nlp/chain-of-thought-bias
공식 코드On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
https://github.com/salt-nlp/dylan
공식 코드A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.