https://github.com/tqfang/complex-commonsense-reasoning
공식 코드Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
Papers with Code 아카이브
Question Answering
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- 평가표
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tranhungnghiep/AnalyzeKGE
공식 코드Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
https://github.com/tranhungnghiep/KG20C
공식 코드Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
https://github.com/tranhungnghiep/MEI-KGE
공식 코드Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
https://github.com/transmuteai/data-efficient-transformers
공식 코드Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
https://github.com/traveler-framework/traveler
공식 코드TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering
https://github.com/tree985/treble-counterfactual-vlms
공식 코드Treble Counterfactual VLMs: A Causal Approach to Hallucination
https://github.com/tri-ml/prismatic-vlms
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/tri-ml/vlm-evaluation
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/trihuynh88/multigrid_mem
공식 코드https://github.com/trunpm/pmt-aaai23
공식 코드Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer
https://github.com/trunpm/tpt-for-videoqa
공식 코드Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering
https://github.com/trust4ai/astral
공식 코드https://github.com/trustworthy-software/quatrain
공식 코드https://github.com/tsinghua-fib-lab/CityEQA
공식 코드CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
https://github.com/tsinghua-msiip/uni-med
공식 코드Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
https://github.com/tsujuifu/pytorch_empirical-mvm
공식 코드An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
https://github.com/tsujuifu/pytorch_violet
공식 코드VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
https://github.com/tsukuba-websci/psilmn
공식 코드https://github.com/tsvm/factcheck-cqa
공식 코드SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
https://github.com/tttamaki/vqd
공식 코드An Entropy Clustering Approach for Assessing Visual Question Difficulty
https://github.com/tttttttty/alcqa
공식 코드https://github.com/tttyuntian/vlm_lexical_grounding
공식 코드Does Vision-and-Language Pretraining Improve Lexical Grounding?
https://github.com/ttumyche/cxreasonbench
공식 코드CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays
https://github.com/tuhinjubcse/faithfuledits_emnlp2023
공식 코드Learning to Follow Object-Centric Image Editing Instructions Faithfully
https://github.com/tuneinsight/federated-llms
공식 코드Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs
https://github.com/turingmotors/nuscenes-mqa
공식 코드https://github.com/turkunlp/paraphrase-span-detection
공식 코드https://github.com/tushar117/Transformer-Models-for-Text-Coherence-Assessment
공식 코드https://github.com/tutujingyugang1/ChatVLA_public
공식 코드ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
https://github.com/tuvuumass/task-transferability
공식 코드https://github.com/twytock/Distinguishing_Cell_Types
공식 코드https://github.com/txchen-ustc/llama-slayer
공식 코드Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection
https://github.com/txh-mercury/cosa
공식 코드COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
https://github.com/tyang816/medchatzh
공식 코드MedChatZH: a Better Medical Adviser Learns from Better Instructions
https://github.com/tychen-sjtu/implicit-videoqa
공식 코드Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.