🥇Top ML Papers of the Week
🥇Top ML Papers of the Week
The top ML Papers of the Week (Jan 8 - Jan 14)
1). InseRF - a method for text-driven generative object insertion in the Neural 3D scenes; it enables users to provide textual descriptions and a 2D bounding box in a reference viewpoint to generate new objects in 3D scenes; InseRF is also capable of controllable and 3D-consistent object insertion without requiring explicit 3D information as input. (paper | tweet)
2). Sleeper Agents - shows that LLMs can learn deceptive behavior that persists through safety training; for instance, an LLM was trained to write secure code for a specified year but given another year can enable exploitable code; this backdoor behavior can persist even when training LLMs with techniques like reinforcement learning and adversarial training. (paper | tweet)
3). Blending Is All You Need - shows that effectively combining existing small models of different sizes (6B/13B parameters) can result in systems that can compete with ChatGPT level performance; the goal is to build a collaborative conversational system that can effectively leverage these models to improve engagement and quality of chat AIs and generate more diverse responses. (paper | tweet)
4). MagicVideo-V2 - proposes an end-to-end video generation pipeline that integrates the text-to-image model, video motion generator, reference image embedding module, and frame interpolation module; it can generate high-resolution video with advanced fidelity and smoothness compared to other leading and popular text-to-video systems. (paper | tweet)
5). Trustworthiness in LLMs - a comprehensive study (100+ pages) of trustworthiness in LLMs, discussing challenges, benchmarks, evaluation, analysis of approaches, and future directions; proposes a set of principles for trustworthy LLMs that span 8 dimensions, including a benchmark across 6 dimensions (truthfulness, safety, fairness, robustness, privacy, and machine ethics); it also presents a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets; while proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, there are a few open-source models that are closing the gap. (paper | tweet)
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6). Prompting LLMs for Table Understanding - a new framework, inspired by Chain-of-Thought prompting, to instruct LLMs to dynamically plan a chain of operations that transforms a complex table to reliably answer the input question; an LLM is used to iteratively generate operations, step-by-step, that will perform necessary transformations to the table (e.g., adding columns or deleting info). (paper | tweet)
7). Jailbreaking Aligned LLMs - proposes 40 persuasion techniques to systematically jailbreak LLMs; their adversarial prompts (also referred to as persuasive adversarial prompts) achieve a 92% attack success rate on aligned LLMs, like Llama 2-7B and GPT-4, without specialized optimization. (paper | tweet)
8). From LLM to Conversational Agents - proposes RAISE, an advanced architecture to enhance LLMs for conversational agents; it's inspired by the ReAct framework and integrates a dual-component memory system; it utilizes a scratchpad and retrieved examples to augment the agent's capabilities; the scratchpad serves as transient storage (akin to short-term memory) and the retrieval module operates as the agent's long-term memory; this system mirrors human short-term and long-term memory and helps to maintain context and continuity which are key in conversational systems. (paper | tweet)
9). Quantifying LLM’s Sensitivity to Spurious Features in Prompt Design - finds that widely used open-source LLMs are extremely sensitive to prompt formatting in few-shot settings; subtle changes in prompt formatting using a Llama 2 13B model can result in a performance difference of up to 76 accuracy points. (paper | tweet)
10). Adversarial Machine Learning - a comprehensive survey that covers the current state of adversarial ML with a proper taxonomy of concepts, discussions, adversarial methods, mitigation tactics, and remaining challenges. (paper | tweet)