A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, et al.
Automated evaluation of ChatGPT and GPT-4 on 25 NLP tasks reveals ~25% average quality loss versus SOTA, with harder tasks showing greater degradation.
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FreeNeRF introduces free frequency regularization to improve few-shot neural rendering by addressing frequency-related challenges in NeRF training.
Chirag Vashist, Shichong Peng, Ke Li
RS-IMLE improves few-shot image synthesis by redesigning the prior distribution to align training and inference latent codes, yielding higher quality images than existing GAN and IMLE methods.
Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli, et al.
This paper proposes a three-agent workflow integrating conversational data collection, structured processing, and LLM-based prediction for travel behavior, achieving 71.5% accuracy with vision-based LLMs.
Arda Sevinc, Abdurrahman Gumus
AutoReason automatically generates chain-of-thought rationales by decomposing implicit queries into explicit questions, improving multi-step reasoning accuracy in weaker LLMs on StrategyQA and HotpotQA.
Adam Zweiger, Jyothish Pari, Han Guo, et al.
SEAL enables LLMs to self-adapt by generating their own finetuning data and update directives, using reinforcement learning to optimize downstream performance.
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This survey systematically reviews in-context learning with retrieved demonstrations, covering methods, benchmarks, and open challenges.
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This paper introduces a few-shot neuro-symbolic imitation learning framework that combines symbolic planning with neural control for long-horizon tasks.
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This paper proposes a method to factorize spatio-temporal foundation models to reduce computational costs while maintaining zero/few-shot generalization performance.
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This paper demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance.
Yisheng Song, Ting Wang, Puyu Cai, et al.
A comprehensive survey of 200+ few-shot learning papers, introducing a knowledge pyramid and analyzing challenges, applications, and future directions.
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This paper introduces Mma, a multi-modal adapter for vision-language models that enhances few-shot transfer learning by integrating visual and textual features.