Semi-structured interviews (SSIs) are a commonly employed data-collection method in healthcare research, offering in-depth qualitative insights into subject experiences. Despite their value, the manual analysis of SSIs is notoriously time-consuming and labor-intensive, in part due to the difficulty of extracting and categorizing emotional responses, and challenges in scaling human evaluation for large populations. In this study, we develop RACER, a Large Language Model (LLM) based expert-guided automated pipeline that efficiently converts raw interview transcripts into insightful domain-relevant themes and sub-themes. We used RACER to analyze SSIs conducted with 93 healthcare professionals and trainees to assess the broad personal and professional mental health impacts of the COVID-19 crisis. RACER achieves moderately high agreement with two human evaluators (72%), which approaches the human inter-rater agreement (77%). Interestingly, LLMs and humans struggle with similar content involving nuanced emotional, ambivalent/dialectical, and psychological statements. Our study highlights the opportunities and challenges in using LLMs to improve research efficiency and opens new avenues for scalable analysis of SSIs in healthcare research.

Humans can quickly learn new behaviors by leveraging background world knowledge. In contrast, agents trained with reinforcement learning (RL) typically learn behaviors from scratch. We thus propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data for embodied RL. We initialize policies with VLMs by using them as promptable representations: embeddings that are grounded in visual observations and encode semantic features based on the VLM's internal knowledge, as elicited through prompts that provide task context and auxiliary information. We evaluate our approach on visually-complex, long horizon RL tasks in Minecraft and robot navigation in Habitat. We find that our policies trained on embeddings extracted from general-purpose VLMs outperform equivalent policies trained on generic, non-promptable image embeddings. We also find our approach outperforms instruction-following methods and performs comparably to domain-specific embeddings.

Large Language Models (LLMs) frequently suffer from knowledge-intensive questions, often being inconsistent by providing different outputs despite given the same input. The response quality worsens when the user expresses a firm opposing stance which causes the LLMs to adjust its response despite the correct initial one. These behaviors decrease the reliability and validity of the responses provided by these models. In this paper, we attempt to 1) raise awareness of the inherent risks that follow from overly relying on AI agents like ChatGPT by showing how Chain-of-Feedback (CoF) triggers LLMs to deviate more from the actual answer and 2) suggest a novel prompting method, Recursive Chain of Feedback (R-CoF), that we are conducting further study. The CoF system takes in an open-ended multi-step question. Then, we repetitively provide meaningless feedback requesting another attempt. Our preliminary experiments show that such feedback only decreases the quality of the response. On the other hand, to mitigate the effects of the aforementioned inconsistencies, we present a novel method of recursively revising the initial incorrect reasoning provided by the LLM by repetitively breaking down each incorrect step into smaller individual problems.

Machine learning (ML) techniques for optimizing data management problems have been extensively studied and widely deployed in recent five years. However traditional ML methods have limitations on generalizability (adapting to different scenarios) and inference ability (understanding the context). Fortunately, large language models (LLMs) have shown high generalizability and human-competitive abilities in understanding context, which are promising for data management tasks (e.g., database diagnosis, database tuning). However, existing LLMs have several limitations: hallucination, high cost, and low accuracy for complicated tasks. To address these challenges, we design LLMDB, an LLM-enhanced data management paradigm which has generalizability and high inference ability while avoiding hallucination, reducing LLM cost, and achieving high accuracy. LLMDB embeds domain-specific knowledge to avoid hallucination by LLM fine-tuning and prompt engineering. LLMDB reduces the high cost of LLMs by vector databases which provide semantic search and caching abilities. LLMDB improves the task accuracy by LLM agent which provides multiple-round inference and pipeline executions. We showcase three real-world scenarios that LLMDB can well support, including query rewrite, database diagnosis and data analytics. We also summarize the open research challenges of LLMDB.

Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting some lack of generalisation ability. This issue has usually been alleviated by feeding more training data into the LLM. However, this method is brittle, as the scope of tasks may not be readily predictable or may evolve, and updating the model with new data generally requires extensive additional training. By contrast, systems, such as causal models, that learn abstract variables and causal relationships can demonstrate increased robustness against changes in the distribution. One reason for this success is the existence and use of Independent Causal Mechanisms (ICMs) representing high-level concepts that only sparsely interact. In this work, we apply two concepts from causality to learn ICMs within LLMs. We develop a new LLM architecture composed of multiple sparsely interacting language modelling modules. We introduce a routing scheme to induce specialisation of the network into domain-specific modules. We also present a Mutual Information minimisation objective that trains a separate module to learn abstraction and domain-invariant mechanisms. We show that such causal constraints can improve out-of-distribution performance on abstract and causal reasoning tasks.

One of the most fundamental problems in machine learning is finding interpretable representations of the functions we learn. The Mobius transform is a useful tool for this because its coefficients correspond to unique importance scores on sets of input variables. The Mobius Transform is strongly related (and in some cases equivalent) to the concept of Shapley value, which is a widely used game-theoretic notion of importance. This work focuses on the (typical) regime where the fraction of non-zero Mobius coefficients (and thus interactions between inputs) is small compared to the set of all $2^n$ possible interactions between $n$ inputs. When there are $K = O(2^{n \delta})$ with $\delta \leq \frac{1}{3}$ non-zero coefficients chosen uniformly at random, our algorithm exactly recovers the Mobius transform in $O(Kn)$ samples and $O(Kn^2)$ time with vanishing error as $K \rightarrow \infty$, the first non-adaptive algorithm to do so. We also uncover a surprising connection between group testing and the Mobius transform. In the case where all interactions are between at most $t = \Theta(n^{\alpha})$ inputs, for $\alpha < 0.409$, we are able to leverage results from group testing to provide the first algorithm that computes the Mobius transform in $O(Kt\log n)$ sample complexity and $O(K\mathrm{poly}(n))$ time with vanishing error as $K \rightarrow \infty$. Finally, we present a robust version of this algorithm that achieves the same sample and time complexity under some assumptions, but with a factor depending on noise variance. Our work is deeply interdisciplinary, drawing from tools spanning across signal processing, algebra, information theory, learning theory and group testing to address this important problem at the forefront of machine learning.

We introduce the concept of multiple temporal perspectives, a novel approach applicable to Recurrent Neural Network (RNN) architectures for enhancing their understanding of sequential data. This method involves maintaining diverse temporal views of previously encountered text, significantly enriching the language models' capacity to interpret context. To show the efficacy of this approach, we incorporate it into the Receptance Weighted Key Value (RWKV) architecture, addressing its inherent challenge of retaining all historical information within a single hidden state. Notably, this improvement is achieved with a minimal increase in the number of parameters --even as little as $0.04\%$ of the original number of parameters. Further, the additional parameters necessary for the multiple temporal perspectives are fine-tuned with minimal computational overhead, avoiding the need for a full pre-training. The resulting model maintains linear computational complexity during prompt inference, ensuring consistent efficiency across various sequence lengths. The empirical results and ablation studies included in our research validate the effectiveness of our approach, showcasing improved performance across multiple benchmarks. The code, model weights and datasets are open-sourced at: https://github.com/RazvanDu/TemporalRNNs.

The transformer architecture from Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. We propose DenseFormer, a simple modification to the standard architecture that improves the perplexity of the model without increasing its size -- adding a few thousand parameters for large-scale models in the 100B parameters range. Our approach relies on an additional averaging step after each transformer block, which computes a weighted average of current and past representations -- we refer to this operation as Depth-Weighted-Average (DWA). The learned DWA weights exhibit coherent patterns of information flow, revealing the strong and structured reuse of activations from distant layers. Experiments demonstrate that DenseFormer is more data efficient, reaching the same perplexity of much deeper transformer models, and that for the same perplexity, these new models outperform transformer baselines in terms of memory efficiency and inference time.

Recent works have explored the use of LLMs for reasoning tasks focussing on relatively simple problems, such as logical question answering. In our work, we wish to tackle more complicated problems, significantly expanding the capabilities of these models. Particularly, we explore whether LLMs can solve challenging first-order combinatorial reasoning problems, an example being the popular puzzle Sudoku. These problems have an underlying first-order structure described by a general description in natural language and can be instantiated to instances of varying sizes. Moreover these problems are computationally intensive requiring several reasoning steps to reach the solution. We present PuzzleBench a dataset of 31 such challenging puzzles. We observe that LLMs even when aided by symbolic solvers perform rather poorly on our benchmark. In response we propose a new approach, Puzzle-LM which combines LLMs with both symbolic solvers and program interpreters enabling them to reason about such challenging problems. We also show how feedback from smaller solved instances can help improve this reasoning ability.

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel enhancements to the conventional time series Transformer architecture, resulting in our proposed Masked Encoder-based Universal Time Series Forecasting Transformer (Moirai). Trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains, Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models. Code, model weights, and data will be released.