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Found 11 papers, 5 papers with code
Reverse Training to Nurse the Reversal Curse
Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse.
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
Ranked #30 on
Question Answering
on TriviaQA
Efficient Tool Use with Chain-of-Abstraction Reasoning
LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1. 4x faster than baseline tool-augmented LLMs.
PathFinder: Guided Search over Multi-Step Reasoning Paths
Using constrained reasoning, PathFinder integrates novel quality constraints, pruning, and exploration methods to enhance the efficiency and the quality of generation.
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
By fine-tuning the System-2 module (LLaMA-2 70B) on only a small amount of data on multi-step reasoning, the accuracy of our method is further improved and surpasses the best fully-supervised end-to-end approach by 5. 7% and a pipeline approach with FlanPaLM (540B) by 7. 5% on a challenging dataset with human-authored questions.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on MS COCO
Shepherd: A Critic for Language Model Generation
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs.
ALERT: Adapting Language Models to Reasoning Tasks
Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning.
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers.
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU
In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN).
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models
With the recent explosion in popularity of voice assistant devices, there is a growing interest in making them available to user populations in additional countries and languages.
