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Found 18 papers, 12 papers with code
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
On single-turn dialogue and summarization, a SAMI-trained mistral-7b outperforms the initial pretrained model, with win rates between 66% and 77%.
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Crucially, these improvements require no fine-tuning on these tasks.
Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency
Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses.
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
In this work, we use a language-model-infused scaffolding program to improve itself.
ContextRef: Evaluating Referenceless Metrics For Image Description Generation
None of the methods is successful with ContextRef, but we show that careful fine-tuning yields substantial improvements.
Hypothesis Search: Inductive Reasoning with Language Models
Because of the prohibitive cost of generation with state-of-the-art LLMs, we consider a middle step to filter the set of hypotheses that will be implemented into programs: we either ask the LLM to summarize into a smaller set of hypotheses, or ask human annotators to select a subset of the hypotheses.
SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT
Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model.
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness
Language model training in distributed settings is limited by the communication cost of gradient exchanges.
Certified Deductive Reasoning with Language Models
In experiments on PrOntoQA, ProofWriter and Syllogism Validity datasets, \textsc{LogicGuide} significantly improves the performance of GPT-3, GPT-3. 5 Turbo and LLaMA (accuracy gains up to 35\%), while drastically reducing \emph{content effects} -- the interference between unwanted prior assumptions and reasoning, which humans and language models suffer from.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
Ranked #7 on
Code Generation
on HumanEval
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics
Few images on the Web receive alt-text descriptions that would make them accessible to blind and low vision (BLV) users.
STaR: Bootstrapping Reasoning With Reasoning
We show that STaR significantly improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers, and performs comparably to fine-tuning a 30$\times$ larger state-of-the-art language model on CommensenseQA.
Ranked #17 on
Common Sense Reasoning
on CommonsenseQA
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
CRUDE: Calibrating Regression Uncertainty Distributions Empirically
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting.
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards.
Contextual Salience for Fast and Accurate Sentence Vectors
We introduce contextual salience (CoSal), a measure of word importance that uses the distribution of context vectors to normalize distances and weights.
