Noisy expert imitation learning requires exponential samples for offline methods but polynomial for a variant of on-policy distillation under a noise condition.
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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abstract
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.
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- abstract As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge di
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representative citing papers
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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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citing papers explorer
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Learning the Signature of Memorization in Autoregressive Language Models
A classifier trained only on transformer fine-tuning data detects an invariant memorization signature that transfers to Mamba, RWKV-4, and RecurrentGemma with AUCs of 0.963, 0.972, and 0.936.
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TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Tiny language models under 10M parameters trained on a synthetic children's story dataset generate fluent, consistent, multi-paragraph English text with near-perfect grammar and reasoning.
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Language Models are Few-Shot Learners
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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Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients
ZPPO improves distillation to small vision-language models by using binary and negative candidate prompts plus a replay buffer for hard questions, outperforming standard distillation and GRPO on a 31-benchmark suite with largest gains at the 0.8B scale.
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MATCHA: Matching Text via Contrastive Semantic Alignment
MATCHA introduces a dual-view contrastive metric measuring proximity to gold text and distance from adversarial contradictions, outperforming ROUGE and BERTScore by up to 20% on TruthfulQA and other NLP benchmarks.
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Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling
RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
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A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
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RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romanian
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Multilingual Multi-Label Emotion Classification at Scale with Synthetic Data
Synthetic data of 1M+ multi-label samples across 23 languages trains models that match or exceed English-only specialists on zero-shot benchmarks for emotion classification.
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Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
OPD for LLMs suffers length inflation and repetition collapse; StableOPD uses reference divergence and rollout mixing to prevent it and improve math reasoning performance by 7.2% on average.
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Kathleen: Oscillator-Based Byte-Level Text Classification Without Tokenization or Attention
Kathleen performs byte-level text classification via recurrent oscillator banks, FFT wavetable encoding, and phase harmonics, matching pretrained baselines on standard benchmarks with 36% fewer parameters.
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Explainable Semantic Textual Similarity via Dissimilar Span Detection
Introduces the Dissimilar Span Detection task and Span Similarity Dataset to explain semantic textual similarity by identifying differing spans between text pairs.
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Accelerating Large Language Model Decoding with Speculative Sampling
Speculative sampling accelerates LLM decoding 2-2.5x by letting a draft model propose short sequences that the target model scores in parallel, then applies modified rejection sampling to keep the exact target distribution.
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Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
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MixSD: Mixed Contextual Self-Distillation for Knowledge Injection
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From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
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Distribution Corrected Offline Data Distillation for Large Language Models
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Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
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Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding
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ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models
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RouteNLP: Closed-Loop LLM Routing with Conformal Cascading and Distillation Co-Optimization
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LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics
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Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch
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Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
A modified divergence decouples top-K teacher probabilities from the distribution tail during distillation, yielding competitive performance on decoder models with standard compute.
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User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
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MiniMax-01: Scaling Foundation Models with Lightning Attention
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GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization
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Retrieval-Augmented Generation for Natural Language Processing: A Survey
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Chain-of-Verification Reduces Hallucination in Large Language Models
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MiniLLM: On-Policy Distillation of Large Language Models
MiniLLM distills large language models into smaller ones via reverse KL divergence and on-policy optimization, yielding higher-quality responses with lower exposure bias than standard KD baselines.
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
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HuggingFace's Transformers: State-of-the-art Natural Language Processing
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Evaluating Pluralism in LLMs through Latent Perspectives
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ProbeScale: Probing Analysis to Optimize Neural Scaling Laws for Efficient Small Language Model Inference
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When Meaning Travels: A Granular Lens on Hybrid-MoE's Role in Idiomatic Understanding for Language Models
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Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral
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Grammatically-Guided Sparse Attention for Efficient and Interpretable Transformers
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PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
PromptRad reformulates multi-label radiology report classification as masked language modeling and enriches verbalizers with UMLS synonyms, outperforming baselines with only 32 training examples.
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Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
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Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?
Fine-tuned LLM and explainable models predict vocabulary difficulty with correlations r > 0.91 and r > 0.77, showing spelling difficulty and test item construction as key influences in addition to word production difficulty.
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ReAD: Reinforcement-Guided Capability Distillation for Large Language Models
ReAD applies a contextual bandit to allocate fixed-token distillation budget across interdependent LLM capabilities, yielding higher task utility and fewer negative spillovers than standard methods.
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G-Loss: Graph-Guided Fine-Tuning of Language Models
G-Loss builds a document-similarity graph and uses semi-supervised label propagation to guide fine-tuning of language models, yielding higher accuracy than standard losses on five classification benchmarks.
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Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Widthwise pruning of LVLM language backbones combined with supervised finetuning and hidden-state distillation recovers over 95% performance using just 5% of data across 3B-7B models.
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A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
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A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
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ALIEN: Aligned Entropy Head for Improving Uncertainty Estimation of LLMs
ALIEN trains a lightweight uncertainty head initialized to model entropy and refined via supervised regularization to improve detection of incorrect predictions and calibration on classification and NER tasks.
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Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments
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Remember what you did so you know what to do next
GPT-J with full action history achieves 3.5x improvement over RL in ScienceWorld and matches a two-stage system using 29x larger models.
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From Sentiment to Actionable Insights: A Data-Driven Public Sentiment Analysis of Advanced Air Mobility
Applies standard sentiment classifiers and topic modeling to a large AAM discussion corpus, identifies six clusters of public concern, and lists strategies to address them.
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Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit
Fine-tuned RoBERTa achieves 0.62 macro-F1 on 900 Reddit comments, outperforming best zero-shot LLM at 0.50, with largest gap on detecting belief propagation.