Mark Coates

2025-05-19

ArXiv (preprint)

Half Search Space is All You Need

Pavel Rumiantsev

2025-05-19

ArXiv (preprint)

Sparse Decomposition of Graph Neural Networks

Yaochen Hu

Mai Zeng

Ge Zhang

Pavel Rumiantsev

Liheng Ma

Yingxue Zhang

2025-03-17

TMLR (accepted)

Refining Answer Distributions for Improved Large Language Model Reasoning

Soumyasundar Pal

Didier Chételat

Yingxue Zhang

Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (see more)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Refined Answer Distributions, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode --- the most likely answer. Empirical evaluation on several reasoning benchmarks demonstrates the superiority of the proposed approach.

2025-03-05

ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (published)

Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation

Pavel Rumiantsev

Neural Architecture Search (NAS) is a powerful automatic alternative to manual design of a neural network. In the zero-shot version, a fast … (see more)ranking function is used to compare architectures without training them. The outputs of the ranking functions often vary significantly due to different sources of randomness, including the evaluated architecture's weights' initialization or the batch of data used for calculations. A common approach to addressing the variation is to average a ranking function output over several evaluations. We propose taking into account the variation in a different manner, by viewing the ranking function output as a random variable representing a proxy performance metric. During the search process, we strive to construct a stochastic ordering of the performance metrics to determine the best architecture. Our experiments show that the proposed stochastic ordering can effectively boost performance of a search on standard benchmark search spaces.

2025-03-04

TMLR (accepted)

Personalized Negative Reservoir for Incremental Learning in Recommender Systems

Antonios Valkanas

Yuening Wang

Yingxue Zhang

2025-02-11

TMLR (accepted)

InnerThoughts: Disentangling Representations and Predictions in Large Language Models

Didier Chételat

Joseph Cotnareanu

Rylee Thompson

Yingxue Zhang

Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts.… (see more) Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultimately, however, only the hidden state corresponding to the final layer and token position is used to predict the answer label. In this work, we propose instead to learn a small separate neural network predictor module on a collection of training questions, that take the hidden states from all the layers at the last temporal position as input and outputs predictions. In effect, such a framework disentangles the representational abilities of LLMs from their predictive abilities. On a collection of hard benchmarks, our method achieves considerable improvements in performance, sometimes comparable to supervised fine-tuning procedures, but at a fraction of the computational cost.

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

MODL: Multilearner Online Deep Learning

Antonios Valkanas

Boris Oreshkin

Online deep learning solves the problem of learning from streams of data, reconciling two opposing objectives: learn fast and learn deep. Ex… (see more)isting work focuses almost exclusively on exploring pure deep learning solutions, which are much better suited to handle the"deep"than the"fast"part of the online learning equation. In our work, we propose a different paradigm, based on a hybrid multilearner approach. First, we develop a fast online logistic regression learner. This learner does not rely on backpropagation. Instead, it uses closed form recursive updates of model parameters, handling the fast learning part of the online learning problem. We then analyze the existing online deep learning theory and show that the widespread ODL approach, currently operating at complexity

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models

Ge Zhang

Mohammad Alomrani

Hongjian Gu

Jiaming Zhou

Yaochen Hu

Bin Wang

Qun Liu

Yingxue Zhang

Jianye Hao

Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (see more)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.

2024-12-23

ArXiv (preprint)

Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models

Ge Zhang

Mohammad Alomrani

Hongjian Gu

Jiaming Zhou

Yaochen Hu

Bin Wang

Qun Liu

Yingxue Zhang

Jianye Hao

2024-12-23

ArXiv (preprint)

Hint Marginalization for Improved Reasoning in Large Language Models

Soumyasundar Pal

Didier Ch'etelat

Yingxue Zhang

Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (see more)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.

2024-12-17

ArXiv (preprint)

Hint Marginalization for Improved Reasoning in Large Language Models

Soumyasundar Pal

Didier Ch'etelat

Yingxue Zhang

Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (see more)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.

2024-12-17

ArXiv (preprint)