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The Quill

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations - Summary

The paper proposes a re-ranking approach for explainable recommender systems using knowledge graphs to optimize for recency, popularity, and diversity of explanations. The approach is evaluated on two public datasets and shows an increase in explanation quality while preserving recommendation utili

SLiC-HF: Sequence Likelihood Calibration with Human Feedback - Summary

The paper presents a new approach called SLiC-HF that uses Sequence Likelihood Calibration with Human Feedback to improve language models. The approach is shown to be effective on the TL;DR summarization task and is a simpler and more computationally efficient alternative to Reinforcement Learning

Boosted Prompt Ensembles for Large Language Models - Summary

The paper proposes a prompt ensembling method for large language models called 'boosted prompting', which uses a small dataset to construct a set of few shot prompts that together comprise a boosted prompt ensemble. The few shot examples for each prompt are chosen in a stepwise fashion to be 'hard'

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance - Summary

The paper discusses the cost associated with querying large language models (LLMs) and proposes FrugalGPT, a framework that uses LLM APIs to process natural language queries within a budget constraint. The framework uses prompt adaptation, LLM approximation, and LLM cascade to reduce the inference

Discovering Language Model Behaviors with Model-Written Evaluations - Summary

The paper explores the use of language models (LMs) to automatically generate evaluations for testing LM behaviors. The generated evaluations are diverse and of high quality, and the approach is significantly cheaper, lower effort, and faster than manual data creation. The paper discovers new cases

We're Afraid Language Models Aren't Modeling Ambiguity - Summary

The paper discusses the importance of managing ambiguity in natural language understanding and evaluates the ability of language models (LMs) to recognize and disentangle possible meanings. The authors present AMBIENT, a linguist-annotated benchmark of 1,645 examples with diverse kinds of ambiguity

Scaling Transformer to 1M tokens and beyond with RMT - Summary

The paper presents a method to extend the context length of BERT, a Transformer-based model in natural language processing, by incorporating token-based memory storage and segment-level recurrence with recurrent memory (RMT). The method enables the model to store task-specific information across up