Seyed Pouyan Mousavi Davoudi

Alireza Amiri-Margavi, Iman Jebellat, Ehsan Jebellat et al.

We propose a collaborative framework in which multiple large language models -- including GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash -- generate and answer complex, PhD-level statistical questions when definitive ground truth is unavailable. Our study examines how inter-model consensus improves both response reliability and identifies the quality of the generated questions. Employing chi-square tests, Fleiss' Kappa, and confidence interval analysis, we quantify consensus rates and inter-rater agreement to assess both response precision and question quality. Key results indicate that Claude and GPT-4 produce well-structured, less ambiguous questions with a higher inter-rater agreement, as shown by narrower confidence intervals and greater alignment with question-generating models. In contrast, Gemini and LLaMA exhibit greater variability and lower reliability in question formulation. These findings demonstrate that collaborative interactions among large language models enhance response reliability and provide valuable insights for optimizing AI-driven collaborative reasoning systems.

Arash Gholami Davoodi, Navid Rezazadeh, Seyed Pouyan Mousavi Davoudi et al.

Large language models (LLMs) must balance diversity and creativity against logical coherence in open-ended generation. Existing truncation-based samplers are effective but largely heuristic, relying mainly on probability mass and entropy while ignoring semantic geometry of the token space. We present Top-W, a geometry-aware truncation rule that uses Wasserstein distance-defined over token-embedding geometry-to keep the cropped distribution close to the original, while explicitly balancing retained probability mass against the entropy of the kept set. Our theory yields a simple closed-form structure for the fixed-potential subset update: depending on the mass-entropy trade-off, the optimal crop either collapses to a single token or takes the form of a one-dimensional prefix that can be found efficiently with a linear scan. We implement Top-W using efficient geometry-based potentials (nearest-set or k-NN) and pair it with an alternating decoding routine that keeps the standard truncation-and-sampling interface unchanged. Extensive experiments on four benchmarks (GSM8K, GPQA, AlpacaEval, and MT-Bench) across three instruction-tuned models show that Top-W consistently outperforms prior state-of-the-art decoding approaches achieving up to 33.7% improvement. Moreover, we find that Top-W not only improves accuracy-focused performance, but also boosts creativity under judge-based open-ended evaluation.