A Survey on Human Preference Learning for Large Language Models
- URL: http://arxiv.org/abs/2406.11191v2
- Date: Tue, 18 Jun 2024 08:18:33 GMT
- Title: A Survey on Human Preference Learning for Large Language Models
- Authors: Ruili Jiang, Kehai Chen, Xuefeng Bai, Zhixuan He, Juntao Li, Muyun Yang, Tiejun Zhao, Liqiang Nie, Min Zhang,
- Abstract summary: The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning.
This survey covers the sources and formats of preference feedback, the modeling and usage of preference signals, as well as the evaluation of the aligned LLMs.
- Score: 81.41868485811625
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancing LLMs with excellent applicability and effectiveness in a wide range of contexts. Despite the numerous related studies conducted, a perspective on how human preferences are introduced into LLMs remains limited, which may prevent a deeper comprehension of the relationships between human preferences and LLMs as well as the realization of their limitations. In this survey, we review the progress in exploring human preference learning for LLMs from a preference-centered perspective, covering the sources and formats of preference feedback, the modeling and usage of preference signals, as well as the evaluation of the aligned LLMs. We first categorize the human feedback according to data sources and formats. We then summarize techniques for human preferences modeling and compare the advantages and disadvantages of different schools of models. Moreover, we present various preference usage methods sorted by the objectives to utilize human preference signals. Finally, we summarize some prevailing approaches to evaluate LLMs in terms of alignment with human intentions and discuss our outlooks on the human intention alignment for LLMs.
Related papers
- Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments [41.25558612970942]
We show that large language models (LLMs) exhibit preference biases and worrying sensitivity to prompt designs.
Motivated by this phenomenon, we propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework, ZEPO.
arXiv Detail & Related papers (2024-06-17T09:48:53Z) - Bayesian Statistical Modeling with Predictors from LLMs [5.5711773076846365]
State of the art large language models (LLMs) have shown impressive performance on a variety of benchmark tasks.
This raises questions about the human-likeness of LLM-derived information.
arXiv Detail & Related papers (2024-06-13T11:33:30Z) - 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) - Do Large Language Models Learn Human-Like Strategic Preferences? [0.0]
We show that Solar and Mistral exhibit stable value-based preference consistent with human in the prisoner's dilemma.
We establish a relationship between model size, value based preference, and superficiality.
arXiv Detail & Related papers (2024-04-11T19:13:24Z) - Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation [28.610190512686767]
Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment.
Existing studies rely on building recommendation models from large-scale users' check-in data.
Recently, the pretrained large language models (LLMs) have achieved significant advancements in various NLP tasks.
arXiv Detail & Related papers (2024-04-02T11:33:04Z) - Nash Learning from Human Feedback [86.09617990412941]
We introduce an alternative pipeline for the fine-tuning of large language models using pairwise human feedback.
We term this approach Nash learning from human feedback (NLHF)
We present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent.
arXiv Detail & Related papers (2023-12-01T19:26:23Z) - Aligning Large Language Models with Human: A Survey [53.6014921995006]
Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks.
Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information.
This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
arXiv Detail & Related papers (2023-07-24T17:44:58Z) - A Survey on Large Language Models for Recommendation [77.91673633328148]
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP)
This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
arXiv Detail & Related papers (2023-05-31T13:51:26Z) - On Learning to Summarize with Large Language Models as References [101.79795027550959]
Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets.
We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
arXiv Detail & Related papers (2023-05-23T16:56:04Z)
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.