paper summaries
This paper proposes a novel way to evaluate large language models (LLMs) that claim to handle long contexts effectively. The researchers introduce a benchmark known as N, enhancing the traditional Needle-in-a-Haystack (NIAH) tests by eliminating literal matches between the search context and the re
paper summaries
The paper introduces a novel framework called Multi-LLM for text summarization, which leverages multiple large language models (LLMs) to generate better summaries, especially for long documents. This framework is designed to overcome the limitations of using a single LLM, which might fail to captur
paper summaries
<p>The research paper introduces <strong>ModernBERT</strong>, an updated version of the original BERT model, which is an encoder-only transformer designed to improve retrieval and classification tasks. Despite the original BERT's widespread use, until now, there have been limited improvements in te
At Portkey.ai, our mission is to empower developers and businesses to leverage the full potential of artificial intelligence in their applications. We're thrilled to announce a significant milestone in this journey: the integration of Google's Vertex AI into our AI gateway. This integration expands your
paper summaries
The paper introduces Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model that outperforms existing models like Llama 2 70B and GPT-3.5 on various benchmarks. It uses a routing network to select two experts per token, allowing access to 47B parameters while actively using only 13B, enhan
paper summaries
This paper presents a method for compressing prompts in large language models (LLMs) to accelerate model inference and reduce cost. The method involves a budget controller, a token-level iterative compression algorithm, and an instruction tuning based method for distribution alignment. Experimental
notes
As developers and founders, you might find yourself asking how does your startup differentiate from ChatGPT. More so, how do you convince a customer to try your product over a generic Chat client.
paper summaries
This paper introduces the Skeleton-of-Thought (SoT) method to decrease the generation latency of large language models (LLMs). SoT guides LLMs to first generate the skeleton of the answer and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point. The m
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This paper provides a comprehensive overview of the current state of knowledge on reasoning in Large Language Models (LLMs), including techniques for improving and eliciting reasoning in these models, methods and benchmarks for evaluating reasoning abilities, findings and implications of previous r
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This paper reviews and compares methods for single-label and multi-label text classification, categorizing them into bag-of-words, sequence-based, graph-based, and hierarchical methods. The findings reveal that pre-trained language models outperform all recently proposed graph-based and hierarchy-b
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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
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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
