GPT-3 shows that scaling an autoregressive language model to 175 billion parameters enables strong few-shot performance across diverse NLP tasks via in-context prompting without fine-tuning.
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Universal language model fine-tuning for text classification
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Transformers reconstruct the constituent RCFTs in tensor-product theories from low-energy spectra, reaching 98% accuracy on WZW models and generalizing to larger central charges with few out-of-domain examples.
OPT releases open decoder-only transformers up to 175B parameters that match GPT-3 performance at one-seventh the carbon cost, along with code and training logs.
Flamingo models reach new state-of-the-art few-shot results on image and video tasks by bridging frozen vision and language models with cross-attention layers trained on interleaved web-scale data.
T5 casts all NLP tasks as text-to-text generation, systematically explores pre-training choices, and reaches strong performance on summarization, QA, classification and other tasks via large-scale training on the Colossal Clean Crawled Corpus.
ALBERT reduces BERT parameters via embedding factorization and layer sharing, adds inter-sentence coherence pretraining, and reaches SOTA on GLUE, RACE, and SQuAD with fewer parameters than BERT-large.
Language models fine-tuned via RL on 5k-60k human preference comparisons produce stylistically better text continuations and human-preferred summaries that sometimes copy input sentences.
Cross-domain memory transfer in coding agents boosts average performance by 3.7% via meta-knowledge like validation routines, with high-level abstractions transferring better than low-level traces.
MiniCPM 1.2B and 2.4B models reach parity with 7B-13B LLMs via model wind-tunnel scaling and a WSD scheduler that yields a higher optimal data-to-model ratio than Chinchilla scaling.
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
FAAST performs test-time supervised adaptation by analytically deriving fast weights from examples in one forward pass, matching backprop performance with over 90% less adaptation time and up to 95% memory savings versus memory-based methods.
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
A GNN-ODE digital twin forecasts reactor thermal-hydraulic states under partial observability, achieving low error on held-out transients and recovering a physical heat-transfer correlation during sim-to-real adaptation.
With better hyperparameters, more data, and longer training, an unchanged BERT-Large architecture matches or exceeds XLNet and other successors on GLUE, SQuAD, and RACE.
Fine-tuning FinBERT on Finnish medical text produces embedding geometry shifts whose correlation with downstream performance the authors attempt to measure as a potential early signal for domain adaptation benefit.
The work provides a reproducible, session-based guide to the NLP pipeline with original adaptations and resources for morphologically rich low-resource languages.
citing papers explorer
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Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
Cross-domain memory transfer in coding agents boosts average performance by 3.7% via meta-knowledge like validation routines, with high-level abstractions transferring better than low-level traces.
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MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
MiniCPM 1.2B and 2.4B models reach parity with 7B-13B LLMs via model wind-tunnel scaling and a WSD scheduler that yields a higher optimal data-to-model ratio than Chinchilla scaling.
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A General Language Assistant as a Laboratory for Alignment
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.