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Found 28 papers, 19 papers with code
Expected Validation Performance and Estimation of a Random Variable’s Maximum
We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #4 on
Multi-task Language Understanding
on MMLU
DataComp-LM: In search of the next generation of training sets for language models
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models.
Language models scale reliably with over-training and on downstream tasks
Second, we relate the perplexity of a language model to its downstream task performance by proposing a power law.
LESS: Selecting Influential Data for Targeted Instruction Tuning
Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots.
Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models
Despite their popularity in non-English NLP, multilingual language models often underperform monolingual ones due to inter-language competition for model parameters.
AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation.
Time is Encoded in the Weights of Finetuned Language Models
We present time vectors, a simple tool to customize language models to new time periods.
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
SILO is built by (1) training a parametric LM on Open License Corpus (OLC), a new corpus we curate with 228B tokens of public domain and permissively licensed text and (2) augmenting it with a more general and easily modifiable nonparametric datastore (e. g., containing copyrighted books or news) that is only queried during inference.
Information Flow Control in Machine Learning through Modular Model Architecture
This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data.
Scaling Expert Language Models with Unsupervised Domain Discovery
Large language models are typically trained densely: all parameters are updated with respect to all inputs.
Editing Models with Task Arithmetic
Changing how pre-trained models behave -- e. g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems.
lo-fi: distributed fine-tuning without communication
When fine-tuning DeiT-base and DeiT-large on ImageNet, this procedure matches accuracy in-distribution and improves accuracy under distribution shift compared to the baseline, which observes the same amount of data but communicates gradients at each step.
M2D2: A Massively Multi-domain Language Modeling Dataset
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs).
Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models
New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use.
kNN-Prompt: Nearest Neighbor Zero-Shot Inference
Retrieval-augmented language models (LMs) use non-parametric memory to substantially outperform their non-retrieval counterparts on perplexity-based evaluations, but it is an open question whether they achieve similar gains in few- and zero-shot end-task accuracy.
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection
Language models increasingly rely on massive web dumps for diverse text data.
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance.
Expected Validation Performance and Estimation of a Random Variable's Maximum
We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
DEMix Layers: Disentangling Domains for Modular Language Modeling
We introduce a new domain expert mixture (DEMix) layer that enables conditioning a language model (LM) on the domain of the input text.
All That's `Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Human evaluations are typically considered the gold standard in natural language generation, but as models{'} fluency improves, how well can evaluators detect and judge machine-generated text?
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text?
Detoxifying Language Models Risks Marginalizing Minority Voices
Language models (LMs) must be both safe and equitable to be responsibly deployed in practice.
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration.
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP.
Show Your Work: Improved Reporting of Experimental Results
Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e. g., accuracy) on held-out test data, compared to previous results.
Variational Pretraining for Semi-supervised Text Classification
We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.
Annotation Artifacts in Natural Language Inference Data
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to.
