5). Attacking Vision-Language Agents via Pop-ups – shows that integrating adversarial pop-ups into existing agent testing environments leads to an attack success rate of 86%; this decreases the agents' task success rate by 47%.
@dair_ai
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Mixtures of In-Context Learners: Expert Weighting for Token Prediction
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4). Mixtures of In-Context Learners – uses subsets of demonstrations to train experts via in-context learning; given a training set, a trainable weighting function is used to combine the experts' next-token predictions…
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OpenAI o1 Model Reasoning Patterns and Performance Analysis
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10). Reasoning Patterns of OpenAI’s o1 Model – when compared with other test-time compute methods, o1 achieved the best performance across most datasets; the authors observe that the most commonly used reasoning patterns in o1 are divide and conquer and self-refinement.
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SynthID-Text: Scalable Watermarking Scheme for LLMs
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9). Scalable Watermarking for LLMs – proposes SynthID-Text, a text-watermarking scheme that can preserve text quality in LLMs, enable high detection accuracy, and minimize latency overhead…https://t.co/isvnHcP610
— DAIR.AI (@dair_ai) 27 octobre 20249). Scalable Watermarking for LLMs – proposes SynthID-Text, a text-watermarking scheme that can preserve text quality in LLMs, enable high detection accuracy, and minimize latency overhead…
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LLMs Reflect Creator Ideology Across Languages
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8). LLMs Reflect the Ideology of their Creators – finds that LLMs exhibit a diverse ideological stance which reflects the worldview of its creators; finds consistent normative differences between how the same LLM responds in Chinese compared to English.
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Feature Steering in LLMs: Evaluating Social Bias Control
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6). Evaluation Feature Steering in LLMs – evaluates featuring steering in LLMs using an experiment that artificially dials up and down various features to analyze changes in model outputs; it focused on 29 features related to social biases and study if feature steering can help
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IBM Granite 3.0 Lightweight Foundation Models for Enterprise
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7). Granite 3.0 – presents lightweight foundation models ranging from 400 million to 8B parameters; supports coding, RAG, reasoning, and function calling, focusing on enterprise use cases, including on-premise and on-device settings.
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Data Synthesis and Augmentation Techniques for Large Language Models
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4). A Survey on Data Synthesis and Augmentation for LLMs – provides a comprehensive summary of data generation techniques in the lifecycle of LLMs; includes discussions on data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications.
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LongRAG Enhances RAG Understanding Long-Context Knowledge
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5). LongRAG – enhances RAG's understanding of long-context knowledge which includes global information and factual details; consists of a hybrid retriever, an LLM-augmented information extractor, a CoT-guided filter, and an LLM-augmented generator…
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Agentic Information Retrieval: LLM Agent Applications
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1). Agentic Information Retrieval – provides an introduction to agentic information retrieval, which is shaped by the capabilities of LLM agents; discusses different types of cutting-edge applications of agentic information retrieval and challenges.