In this work, we analyze various scaling limits of the training dynamics of transformer models in the feature learning regime. We identify the set of parameterizations that admit well-defined infinite width and depth limits, allowing the attention layers to update throughout training--a relevant notion of feature learning in these models. We then use tools from dynamical mean field theory (DMFT) to analyze various infinite limits (infinite key/query dimension, infinite heads, and infinite depth) which have different statistical descriptions depending on which infinite limit is taken and how attention layers are scaled. We provide numerical evidence of convergence to the limits and discuss how the parameterization qualitatively influences learned features.

Moreover, the program committee comprised researchers from 12 countries across five continents. The workshop featured six keynote speakers, oral sessions, poster sessions, a panel discussion, and a networking lunch. Of the 20 submitted papers, six were selected for oral and poster presentation, and an additional nine were selected for poster presentation only. The acceptance rate was, therefore, 75%. All accepted papers are published in the open-access workshop's proceedings at https://ceur-ws.org/Vol-3649/.

A major outage struck Bing.com, Microsoft's search engine, early Thursday with the problem apparently spreading to the brand's application programming interface which means that services such as DuckDuckGo also went down. According to reports the outage also impacts ChatGPT and Ecosia. Despite Google's dominance in the world of web searching, Bing's API has numerous high profile clients. DuckDuckGo issued a brief statement on X. Users were greeted with an error page featuring a panda on Bing.com with the message: 'It's not you, it's us.' 'Announcement: We're currently experiencing an issue with DuckDuckGo Search that might prevent you from getting results.

This article was featured in the One Story to Read Today newsletter. Let's get this out of the way: OpenAI's voice assistant doesn't sound that much like Scarlett Johansson. The movie star has alleged that, though she rebuffed multiple attempts by Sam Altman, the company's CEO, to license her voice for the product that it demoed last week, the one it ended up using was "eerily similar" to her own. Not everyone finds the similarity so eerie--to my ear, it lacks her distinctive smoky rasp--but at the very least, the new AI does appear to imitate the playful lilts and cadences that Johansson used while playing Samantha, the digital assistant in the 2013 film Her. That's depressing--and not only because OpenAI may have run roughshod over Johansson's wishes, but because it has made such an unimaginative choice.

OpenAI cast the actor of Sky's voice months before Sam Altman contacted Scarlett Johansson, and it had no intention of finding someone who sounded like her, according to The Washington Post. The publication said the flier OpenAI issued last year looked for actors that had "warm, engaging [and] charismatic" voices. They needed to be between 25 and 45 years old and had to be non-union, but OpenAI reportedly didn't specify that it was looking for a Scarlett Johansson voice-alike. If you'll recall, Johansson accused the company of copying her likeness without permission for its Sky voice assistant. The agent of Sky's voice told The Post that the company never talked about Johansson or the movie Her with their talent.

Reinforcement Learning (RL) suffers from sample inefficiency in sparse reward domains, and the problem is pronounced if there are stochastic transitions. To improve the sample efficiency, reward shaping is a well-studied approach to introduce intrinsic rewards that can help the RL agent converge to an optimal policy faster. However, designing a useful reward shaping function specific to each problem is challenging, even for domain experts. They would either have to rely on task-specific domain knowledge or provide an expert demonstration independently for each task. Given, that Large Language Models (LLMs) have rapidly gained prominence across a magnitude of natural language tasks, we aim to answer the following question: Can we leverage LLMs to construct a reward shaping function that can boost the sample efficiency of an RL agent? In this work, we aim to leverage off-the-shelf LLMs to generate a guide policy by solving a simpler deterministic abstraction of the original problem that can then be used to construct the reward shaping function for the downstream RL agent. Given the ineffectiveness of directly prompting LLMs, we propose MEDIC: a framework that augments LLMs with a Model-based feEDback critIC, which verifies LLM-generated outputs, to generate a possibly sub-optimal but valid plan for the abstract problem. Our experiments across domains from the BabyAI environment suite show 1) the effectiveness of augmenting LLMs with MEDIC, 2) a significant improvement in the sample complexity of PPO and A2C-based RL agents when guided by our LLM-generated plan, and finally, 3) pave the direction for further explorations of how these models can be used to augment existing RL pipelines.

The advancement of large language models has significantly improved natural language processing. However, challenges such as jailbreaks (prompt injections that cause an LLM to follow instructions contrary to its intended use), hallucinations (generating incorrect or misleading information), and comprehension errors remain prevalent. In this report, we present a comparative analysis of the performance of fifteen distinct models, with each model undergoing a standardized test comprising 38 queries across three key metrics: jailbreaks, hallucinations, and comprehension errors. The models are assessed based on the total occurrences of jailbreaks, hallucinations, and comprehension errors. Our work exposes these models' inherent vulnerabilities and challenges the notion of human-level language comprehension of these models. We have empirically analysed the impact of non-standard Unicode characters on LLMs and their safeguarding mechanisms on the best-performing LLMs, including GPT-4, Gemini 1.5 Pro, LlaMA-3-70B, and Claude 3 Opus. By incorporating alphanumeric symbols from Unicode outside the standard Latin block and variants of characters in other languages, we observed a reduction in the efficacy of guardrails implemented through Reinforcement Learning Human Feedback (RLHF). Consequently, these models exhibit heightened vulnerability to content policy breaches and prompt leakage. Our study also suggests a need to incorporate non-standard Unicode text in LLM training data to enhance the capabilities of these models.

Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually based on short-sighted heuristics (e.g., log probability or value function scores) that potentially lead to suboptimal or even distracting subgoals, preventing us from finding longer proofs. To address this challenge, we propose POETRY (PrOvE Theorems RecursivelY), which proves theorems in a recursive, level-by-level manner in the Isabelle theorem prover. Unlike previous step-by-step methods, POETRY searches for a verifiable sketch of the proof at each level and focuses on solving the current level's theorem or conjecture. Detailed proofs of intermediate conjectures within the sketch are temporarily replaced by a placeholder tactic called sorry, deferring their proofs to subsequent levels. This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels. Experiments are conducted on the miniF2F and PISA datasets and significant performance gains are observed in our POETRY approach over state-of-the-art methods. POETRY on miniF2F achieves an average proving success rate improvement of 5.1%. Moreover, we observe a substantial increase in the maximum proof length found by POETRY, from 10 to 26.

Large Language Models (LLMs) have significantly advanced natural language processing tasks such as machine translation, text generation, and sentiment analysis. However, their large size, often consisting of billions of parameters, poses challenges for storage, computation, and deployment, particularly in resource-constrained environments like mobile devices and edge computing platforms. Effective compression and quantization techniques are crucial for addressing these issues, reducing memory footprint and computational requirements without significantly compromising performance. Traditional methods that uniformly map parameters to compressed spaces fail to account for the uneven distribution of parameters, leading to substantial accuracy loss. In this work, we propose Athena, a novel algorithm for efficient block-wise post-training quantization of LLMs. Athena leverages Second-Order Matrix Derivative Information to guide the quantization process using the curvature information of the loss landscape. By grouping parameters by columns or rows and iteratively optimizing the quantization process, Athena updates the model parameters and Hessian matrix to achieve significant compression while maintaining high accuracy. This makes Athena a practical solution for deploying LLMs in various settings.

We introduce RE-Adapt, an approach to fine-tuning large language models on new domains without degrading any pre-existing instruction-tuning. We reverse engineer an adapter which isolates what an instruction-tuned model has learned beyond its corresponding pretrained base model. Importantly, this requires no additional data or training. We can then fine-tune the base model on a new domain and readapt it to instruction following with the reverse engineered adapter. RE-Adapt and our low-rank variant LoRE-Adapt both outperform other methods of fine-tuning, across multiple popular LLMs and datasets, even when the models are used in conjunction with retrieval-augmented generation.