Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends
Summarized by: Sophia Carter [
arxiv.org]
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Recent advancements in large language models (LLMs) have significantly improved summarization systems, but concerns about “hallucination”—generating information not supported by the source—persist. This paper benchmarks the faithfulness of LLMs in dialogue summarization, focusing on GPT-4 and Alpaca-13B. It categorizes errors in summaries, introducing “Circumstantial Inference” for plausible but unsupported statements inferred from conversation context. The study reveals that LLMs often make such inferences, a behavior less common in older models, and proposes a refined error taxonomy. Human annotations show that about 30% of LLM-generated dialogue summaries contain inconsistencies, compared to less than 5% in news summaries. The paper also evaluates automatic error detection methods, finding that they struggle with nuanced errors like circumstantial inferences. To address this, two prompt-based approaches for fine-grained error detection are introduced, outperforming existing metrics. The study highlights the need for improved metrics to capture the evolving error distributions in LLM-generated summaries.
Wings: Learning Multimodal LLMs without Text-only Forgetting
Summarized by: Sophia Carter [ arxiv.org]
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WINGS is a novel Multimodal Large Language Model (MLLM) designed to address a common issue where MLLMs forget how to respond to text-only instructions after being trained on multimodal (text and image) data. Traditional MLLMs align images with text and fine-tune on mixed inputs, but this often leads to a performance drop in text-only tasks. WINGS tackles this by introducing extra modules called “visual” and “textual” learners, which work in parallel within each layer’s attention block to balance the focus on both visual and textual elements. These learners are connected through a mechanism called Low-Rank Residual Attention (LoRRA), which ensures high efficiency by maintaining low computational costs.
The model’s architecture allows it to excel in both text-only and multimodal tasks. WINGS was tested on a new benchmark, the Interleaved Image-Text (IIT) benchmark, which includes a variety of tasks ranging from text-rich to multimodal-rich scenarios. The results show that WINGS outperforms other equally-scaled MLLMs in both text-only and visual question-answering tasks. This makes WINGS a versatile and efficient solution for maintaining robust performance across different types of inputs.
Summarized by: Sophia Carter [ arxiv.org]
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The paper introduces a novel method called Quantized Johnson-Lindenstrauss (QJL) transform for compressing the Key-Value (KV) cache in large language models (LLMs). Traditional quantization methods for KV caches face significant memory overhead because they require storing quantization constants. QJL addresses this by using a Johnson-Lindenstrauss (JL) transform followed by sign-bit quantization, eliminating the need for storing these constants. This method provides an unbiased estimator for the inner product of two vectors with minimal distortion, crucial for maintaining accuracy in attention mechanisms.
The QJL transform applies a random Gaussian projection to key embeddings and then quantizes the result to a single bit (the sign bit). This approach reduces memory usage by over fivefold without compromising accuracy, as demonstrated in various NLP tasks using Llama-2 models. The paper also discusses the implementation of QJL using a lightweight CUDA kernel, which optimizes computation and speeds up runtime.
Experimental results show that QJL significantly reduces memory usage while maintaining high accuracy, outperforming existing methods in long-context question-answering tasks. The proposed method is efficient, data-oblivious, and can be easily parallelized, making it suitable for real-time applications in LLMs. The code for QJL is available on GitHub.
This AI Paper from Databricks and MIT Propose Perplexity-Based Data Pruning: Improving 3B Parameter Model Performance and Enhancing Language Models - MarkTechPost
Summarized by: Liam Nguyen [www.marktechpost.com]
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In machine learning, improving large language models (LLMs) involves enhancing pretraining data quality. Data pruning, a method to select high-quality data subsets, is crucial. Traditional methods like rules-based filtering are limited for large datasets. Researchers from Databricks, MIT, and DatologyAI propose using small reference models to compute text sample perplexity, which measures prediction accuracy. Lower perplexity scores indicate higher-quality data. This method involves training a small model to evaluate perplexity, then pruning the dataset based on these scores. This approach significantly improves LLM performance, reducing training steps and enhancing efficiency across various data compositions and training regimes.
Twelve Labs raises $50M for multimodal AI foundation models
Summarized by: Aria Patel [siliconangle.com]
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Twelve Labs Inc. has secured $50 million in Series A funding, co-led by New Enterprise Associates and Nvidia’s NVentures, with participation from previous investors. The company, which specializes in generative AI models for video understanding, plans to nearly double its staff and focus on R&D. Twelve Labs’ models enable users to locate specific video moments and generate text summaries or detailed reports. Their flagship models, Marengo-2.6 and Pegasus-1, support multimodal tasks across video, audio, and image data. Additionally, a new Embeddings API allows developers to integrate these capabilities into their applications.
Unleashing the Potential of Generative AI in Azure SQL Database
Summarized by: Aria Patel [devblogs.microsoft.com]
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Generative AI integrated into Azure SQL Database is revolutionizing customer interactions by leveraging unique datasets to create personalized experiences. The article demonstrates using Retrieval Augmented Generation (RAG) to build applications that resonate with customer needs. It outlines a process involving vector embeddings and similarity searches, exemplified by a Walmart shopping app that suggests products for specific occasions. By embedding product data and using Azure Open AI, the system provides relevant, data-driven recommendations. This approach enhances customer experience by utilizing proprietary data, making AI a powerful equalizer and data the ultimate differentiator.
Penn uses AI to uncover antibiotics in microbial dark matter
Summarized by: Aria Patel [www.pennmedicine.org]
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Penn Medicine researchers have leveraged AI to significantly advance antibiotic discovery by mining genomic data from the global microbiome. Their study, published in Cell, utilized machine learning to analyze tens of thousands of bacterial genomes, identifying nearly one million potential antibiotic compounds. Initial tests showed dozens of these compounds effectively combating disease-causing bacteria, including antibiotic-resistant strains. The study’s success underscores AI’s transformative role in accelerating drug discovery, which traditionally took years but now can be achieved in hours. The team has made their findings publicly accessible through the AMPSphere repository.
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Technical details
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Editor: Ava Thompson
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