LILO: Bayesian Optimization with Interactive Natural Language Feedback

• URL: http://arxiv.org/abs/2510.17671v1
• Date: Mon, 20 Oct 2025 15:41:56 GMT
• Title: LILO: Bayesian Optimization with Interactive Natural Language Feedback
• Authors: Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham, Zhuokai Zhao, Maximilian Balandat, Eytan Bakshy,
• Abstract summary: We propose a language-in-the-loop framework that uses a large language model (LLM) to convert unstructured feedback into utility signals.<n>We show that this hybrid method is a more natural interface to the decision maker but also outperforms conventional BO baselines.
• Score: 17.560651032728714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For many real-world applications, feedback is essential in translating complex, nuanced, or subjective goals into quantifiable optimization objectives. We propose a language-in-the-loop framework that uses a large language model (LLM) to convert unstructured feedback in the form of natural language into scalar utilities to conduct BO over a numeric search space. Unlike preferential BO, which only accepts restricted feedback formats and requires customized models for each domain-specific problem, our approach leverages LLMs to turn varied types of textual feedback into consistent utility signals and to easily include flexible user priors without manual kernel design. At the same time, our method maintains the sample efficiency and principled uncertainty quantification of BO. We show that this hybrid method not only provides a more natural interface to the decision maker but also outperforms conventional BO baselines and LLM-only optimizers, particularly in feedback-limited regimes.

Related papers

• LLMize: A Framework for Large Language Model-Based Numerical Optimization [0.0]
  Large language models (LLMs) have recently shown strong reasoning capabilities beyond traditional language tasks.<n>This paper presents LLMize, an open-source Python framework that enables LLM-driven optimization.
  arXiv  Detail & Related papers  (2025-12-30T20:05:30Z)
• LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection [8.355352653283516]
  We introduce LISTEN, a framework that leverages a Large Language Model (LLM) as a zero-shot preference oracle.<n>We show LISTEN-U excels when preferences are parametrically aligned, while LISTEN-T offers more robust performance.<n>This work explores a promising direction for steering complex multi-objective decisions directly with natural language.
  arXiv  Detail & Related papers  (2025-10-29T03:17:37Z)
• POPI: Personalizing LLMs via Optimized Natural Language Preference Inference [42.25870704040321]
  POPI is a general framework that introduces a preference inference model to distill heterogeneous user signals into concise natural language summaries.<n>These summaries act as transparent, compact, and transferable personalization representations that condition a shared generation model to produce personalized responses.<n>Extensive experiments across four personalization benchmarks demonstrate that POPI consistently improves personalization accuracy while reducing context overhead by a large margin.
  arXiv  Detail & Related papers  (2025-10-17T23:07:57Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time [50.41806216615488]
  Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora. To make LLMs more usable, aligning them with human preferences is essential. We propose an effective method, textbf MetaAlign, which aims to help LLMs dynamically align with various explicit or implicit preferences specified at inference time.
  arXiv  Detail & Related papers  (2024-10-18T05:31:13Z)
• Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
  We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
  arXiv  Detail & Related papers  (2024-05-31T14:21:04Z)
• Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
  We introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
  arXiv  Detail & Related papers  (2024-05-26T00:29:04Z)
• Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation [18.550311424902358]
  Large language models (LLMs) enable fully natural language (NL) PE dialogues. We propose a novel NL-PE algorithm, PEBOL, which uses Natural Language Inference (NLI) between user preference utterances and NL item descriptions. We numerically evaluate our methods in controlled simulations, finding that PEBOL can achieve an MRR@10 of up to 0.27 compared to the best monolithic LLM baseline's MRR@10 of 0.17.
  arXiv  Detail & Related papers  (2024-05-02T03:35:21Z)
• Bayesian Preference Elicitation with Language Models [82.58230273253939]
  We introduce OPEN, a framework that uses BOED to guide the choice of informative questions and an LM to extract features. In user studies, we find that OPEN outperforms existing LM- and BOED-based methods for preference elicitation.
  arXiv  Detail & Related papers  (2024-03-08T18:57:52Z)
• Large Language Models to Enhance Bayesian Optimization [57.474613739645605]
  We present LLAMBO, a novel approach that integrates the capabilities of Large Language Models (LLM) within Bayesian optimization. At a high level, we frame the BO problem in natural language, enabling LLMs to iteratively propose and evaluate promising solutions conditioned on historical evaluations. Our findings illustrate that LLAMBO is effective at zero-shot warmstarting, and enhances surrogate modeling and candidate sampling, especially in the early stages of search when observations are sparse.
  arXiv  Detail & Related papers  (2024-02-06T11:44:06Z)

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.