Addressing LLM Diversity by Infusing Random Concepts

• URL: http://arxiv.org/abs/2601.18053v1
• Date: Mon, 26 Jan 2026 00:53:28 GMT
• Title: Addressing LLM Diversity by Infusing Random Concepts
• Authors: Pulin Agrawal, Prasoon Goyal,
• Abstract summary: Large language models (LLMs) are known to produce outputs with limited diversity.<n>In this work, we study whether infusing random concepts in the prompts can improve the diversity of the generated outputs.
• Score: 0.3951835393164164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are known to produce outputs with limited diversity. In this work, we study whether infusing random concepts in the prompts can improve the diversity of the generated outputs. To benchmark the approach, we design a systematic evaluation protocol which involves prompting an LLM with questions of the form "Name 10 Hollywood actors", and analyzing diversity measures of the resulting LLM outputs. Our experiments on multiple LLMs show that prepending random words/sentences unrelated to the prompt result in greater diversity in the outputs of LLMs. We believe that this promising result and the evaluation protocol opens up interesting avenues for future work, such as how infusing randomness into LLMs could be applied to other domains. Further, the evaluation protocol could also inspire research into benchmarking LLM diversity more systematically.

Related papers

• Training-free LLM Verification via Recycling Few-shot Examples [11.796208194946141]
  ReFeri evaluates the generated outputs by combining two different scores, motivated from Bayes' rule, and subsequently selects the candidate.<n>Experiments with three different LLMs and across seven diverse tasks demonstrate that our framework significantly improves the accuracy of LLMs.
  arXiv  Detail & Related papers  (2025-06-08T10:02:07Z)
• Reasoning-Aligned Perception Decoupling for Scalable Multi-modal Reasoning [95.44766931218896]
  Multi-modal large language models (MLLMs) still lag behind text-based reasoning.<n>We introduce Perception-Reasoning Decoupling, which modularizes the MLLM's reasoning component and makes it easily replaceable.<n>We propose a novel reinforcement learning algorithm called Visual Perception Optimization (VPO) to align the MLLM's perceptual output with the final reasoning task.
  arXiv  Detail & Related papers  (2025-06-05T02:28:07Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse.<n>Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration.<n>To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• In-Context Learning with Reinforcement Learning for Incomplete Utterance Rewriting [33.89176174108559]
  In-context learning of large language models (LLMs) makes predictions only based on instructions augmented with a few examples. Existing example selection methods for ICL utilize sparse or dense retrievers and derive effective performance. We propose our policy-based reinforcement learning framework for example selection (RLS), which consists of a language model (LM) selector and an LLM generator.
  arXiv  Detail & Related papers  (2024-08-23T12:32:12Z)
• PEDAL: Enhancing Greedy Decoding with Large Language Models using Diverse Exemplars [1.450405446885067]
  Self-ensembling techniques with diverse reasoning paths have demonstrated remarkable performance gains in text generation with Large Language Models (LLMs) We introduce PEDAL, a hybrid self-ensembling approach that combines the strengths of diverse exemplar based prompts and LLM based aggregation to achieve improvement in overall performance.
  arXiv  Detail & Related papers  (2024-08-16T17:54:09Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators.<n>The question of how reliable these evaluators are has emerged as a crucial research question.<n>We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration [39.35476224845088]
  Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling. We propose a training-free ensemble framework DeePEn, fusing the informative probability distributions yielded by different LLMs at each decoding step.
  arXiv  Detail & Related papers  (2024-04-19T08:52:22Z)
• Bridging the Gap between Different Vocabularies for LLM Ensemble [10.669552498083709]
  vocabulary discrepancies among various large language models (LLMs) have constrained previous studies. We propose a novel method to Ensemble LLMs via Vocabulary Alignment (EVA) EVA bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step.
  arXiv  Detail & Related papers  (2024-04-15T06:28:20Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.