AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Beyond Preference in AI Alignment
challenges the dominant practice of AI alignment known as human preference tuning; explains in what ways human preference tuning fails to capture the thick semantic content of human values; argues that AI alignment needs reframing, instead of aligning on human preferences, AI should align on normative standards appropriate to their social roles.
LLM-Based Agents for Software Engineering
a survey paper on LLM-based agents for software engineering, covering perspectives ranging from requirement engineering to test generation to software maintenance.
GameGen
a game engine powered by a diffusion model that enables real-time interaction with complex environments over long trajectories; uses a two-phase training process involving an RL agent to learn and a diffusion model to generate frames; it can interactively simulate DOOM over at 20 fps on a single TPU.
Agentic RAG for Time Series Analysis
proposes an agentic RAG framework for time series analysis; uses a multi-agent architecture where an agent orchestrates specialized sub-agents to complete time-series tasks; the sub-agents leverage tuned small language models and can retrieve relevant prompts containing knowledge about historical patterns and trends; this helps to improve predictions on new data.
AutoGen Studio
a low-code interface for rapidly prototyping AI agents. It's built on top of the AutoGen framework and can also be used for debugging and evaluating multi-agent workflows.
Persuasion Games with LLMs
claims that a multi-agent framework can be used to improve the persuasive efficacy of LLMs; the primary agent engages in persuasive dialogue while auxiliary agents perform key tasks like response analysis and information retrieval; finds that LLMs are capable of creating a perspective change in the users and persuading them to make a purchase decision; for instance, Sales agents can achieve a 71% positive shift in user perspectives.
Smaller, Weaker, Yet Better
finds that weaker + cheaper (WC) models can generate better synthetic data for fine-tuning models compared to data generated with stronger but more expensive models; overall, results suggest that WC models may be a compute-optimal approach for training advanced LLM reasoners.
Transfusion
presents a training recipe to train multi-modal models over discrete and continuous data; combines next token prediction with diffusion to train transformer models over mixed-modality sequences; shows that it’s possible to scale from 7B parameter models to 2T multi-modal tokens that can compete in performance with similar scale diffusion and language models.
ReMamba
investigates the long-context capabilities and efficiencies of Mamba models; the long-context deficiency issues are due to Mamba's RNN-like nature; it achieves this by condensing information via the following compression strategy: the top-k hidden states during the first forward pass and leverages Mamba’s selective mechanism to incorporate them into the state space during the second forward pass; achieves a 3.2 improvement over the baseline on LongBench and 1.6 improvement on L-Eval; the strategy seems to also transfer to Mamba 2.
Text2SQL is Not Enough
proposes Table-Augmented Generation (TAG), a unified framework for answering natural language questions over databases; it represents a wider range of unexplored interactions between LLMs and databases; develops a benchmark and finds that standard methods answer no more than 20% of queries correctly.
Foundation Models for Music
provides a comprehensive overview of state-of-the-art pre-trained models and foundation models in music.
Guide to Continual Multimodal Pretraining
a comprehensive guide on continual multimodal pertaining; introduces FoMo-In-Flux, a large-scale fine-grained and long horizon continual pretraining benchmark.
Automate Design of Agentic Systems
presents Meta Agent Search, a meta agent that iteratively programs and tests new agents based on a growing archive of previous discoveries; claims that with their approach it is possible to learn any possible agentic system including prompts, tool use, control flows, and more; they achieve this by focusing on three main components referred to as search space (define agents), search algorithm (explore search space), and the evaluation function (evaluate candidate agents).
LLM Pruning and Distillation in Practice
provides a comprehensive report on effective methods for compressing Llama 3.1 and Mistral NeMo models; it presents pruning and distillation approaches applied to the original models to produce 4B and 8B parameter models, respectively; before pruning, they also fine-tune the teacher model on their datasets leading to better distillation; their compression strategy yields a state-of-the-art 8B model (MN-Minitron-8B) which outperforms all similarly-sized models on common language modeling benchmarks.
Vizier Gaussian Process Bandit Algorithm
presents Vizier, an algorithm based on Gaussian process bandit optimization used by Google for millions of optimizations and research; it provides an open-source Python implementation of the Vizier algorithm, including benchmarking results that demonstrate its wider applicability.
Language Modeling on Tabular Data
presents a comprehensive survey of language modeling techniques for tabular data; includes topics such as categorization of tabular data structures and data types, datasets used for model training and evaluation, modeling techniques and training objectives, data processing methods, popular architectures, and challenges and future research directions.
Enhancing Robustness in LLMs
proposes a two-stage prompting technique to remove irrelevant information from context; it serves as a self-mitigation process that first identifies the irrelevant information and then filters it out; this leads to enhancement in robustness of the model and overall better performance on reasoning tasks.
A Comprehensive Overview of GraphRAG Methods
focuses on techniques applied to the GraphRAG workflow (graph-based indexing, graph-guided retrieval, and graph-enhanced generation); examines tasks, applications, evaluation, and industrial use cases of GraphRAG.
MagicDec
shows how speculative decoding can enhance throughput, reduce latency, and maintain accuracy in long context generation scenarios; it finds that as sequence length and batch size increase, bottlenecks shift from compute-bound to memory-bound; using these insights, they show it's possible to more effectively use speculative decoding for longer sequences, even when using large batch sizes.
Controllable Text Generation for LLMs
provides a comprehensive survey on methods for controllable text generation in LLMs; discusses issues like safety, consistency, style, and helpfulness.
PEDAL
uses a hybrid self-ensembling approach (based on diverse exemplars) to improve the overall performance of LLMs; specifically, it uses diverse exemplars to generate multiple candidate responses and then aggregates them using an LLM to generate a final response; this approach achieves better accuracy compared to greedy decoding and lower cost compared to self-consistency approaches.
Challenges and Responses in the Practice of LLMs
curates a set of important questions with insightful answers; questions are categorized across topics such as infrastructure, software architecture, data, application, and brain science.
The AI Scientist
a novel AI agent that can develop and write a full conference-level scientific paper costing less than $15; it automates scientific discovery by enabling frontier LLMs to perform independent research and summarize findings; it also uses an automated reviewer to evaluate the generated papers; claims to achieve near-human performance in evaluating paper scores; claims to produce papers that exceed the acceptance threshold at a top machine learning conference as judged by their automated reviewer.
Grok-2
a new frontier model with strong code, math, and reasoning capabilities which includes a large and small model; outperforms both Claude 3.5 Sonnet and GPT-4-Turbo on the LMSYS Chatbot Arena; claims to improve capabilities including instruction following, retrieval, tool use, and enhancing factuality; competes with Claude 3.5 Sonnet (June release) and GPT-4o (May release) on MMLU and HumanEval.