Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration

Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. In this work, we let a single model "step outside the box" by engaging multiple models to correct each other. We introduce a multi-agent collaboration strategy that emulates the academic peer review process. Each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews. Upon receiving peer reviews, agents revise their initial solutions. Extensive experiments on three different types of reasoning tasks show that our collaboration approach delivers superior accuracy across all ten datasets compared to existing methods. Further study demonstrates the effectiveness of integrating confidence in the reviews for math reasoning, and suggests a promising direction for human-mimicking multi-agent collaboration process.

Generative Multimodal Entity Linking

Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base (e.g., Wikipedia). Prior MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters, which can be prohibitively costly and difficult to scale in the era of Large Language Models (LLMs). In this work, we propose GEMEL, a simple yet effective Generative Multimodal Entity Linking method, which leverages the capabilities of LLMs from large-scale pre-training to directly generate target entity names. We keep the vision and language model frozen and only train a linear layer to enable cross-modality interactions. To adapt LLMs to the MEL task, we take advantage of the emerging in-context learning (ICL) capability of LLMs by retrieving multimodal instances as demonstrations. Extensive experiments show that with only ~0.3% of the model parameters fine-tuned, GEMEL achieves state-of-the-art results on two well-established MEL datasets (4.1% accuracy gains on WikiDiverse and 15.4% accuracy gains on WikiMEL). Our approach is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution for utilizing LLMs in the MEL task.