Soft prompt tuning is a widely studied parameter-efficient fine-tuning
method. However, it has a clear drawback: many soft tokens must be inserted
into the input sequences to guarantee downstream performance. As a result, soft
prompt tuning is less considered than Low-rank adaptation (LoRA) in the large
language modeling (LLM) era. In this work, we propose a novel prompt tuning
method, Instruction-Aware Prompt Tuning (IAPT), that requires only four soft
tokens. First, we install a parameter-efficient soft prompt generator at each
Transformer layer to generate idiosyncratic soft prompts for each input
instruction. The generated soft prompts can be seen as a semantic summary of
the input instructions and can effectively guide the output generation. Second,
the soft prompt generators are modules with a bottleneck architecture
consisting of a self-attention pooling operation, two linear projections, and
an activation function. Pilot experiments show that prompt generators at
different Transformer layers require different activation functions. Thus, we
propose to learn the idiosyncratic activation functions for prompt generators
automatically with the help of rational functions. We have conducted
experiments on various tasks, and the experimental results demonstrate that (a)
our IAPT method can outperform the recent baselines with comparable tunable
parameters. (b) Our IAPT method is more efficient than LoRA under the
single-backbone multi-tenant setting.

我们提出了一种新的提示调整方法，Instruction-Aware Prompt Tuning (IAPT)，该方法只需要四个软标记，并且在具有可调参数的情况下优于最近的基准方法，并比低秩适应性 (LoRA) 在单主干多租户设置下更高效。

IAPT：面向大型语言模型的指令感知提示调整

IAPT: Instruction-Aware Prompt Tuning for Large Language Models

Recent breakthroughs in large language modeling have facilitated rigorous
exploration of their application in diverse tasks related to tabular data
modeling, such as prediction, tabular data synthesis, question answering, and
table understanding. Each task presents unique challenges and opportunities.
However, there is currently a lack of comprehensive review that summarizes and
compares the key techniques, metrics, datasets, models, and optimization
approaches in this research domain. This survey aims to address this gap by
consolidating recent progress in these areas, offering a thorough survey and
taxonomy of the datasets, metrics, and methodologies utilized. It identifies
strengths, limitations, unexplored territories, and gaps in the existing
literature, while providing some insights for future research directions in
this vital and rapidly evolving field. It also provides relevant code and
datasets references. Through this comprehensive review, we hope to provide
interested readers with pertinent references and insightful perspectives,
empowering them with the necessary tools and knowledge to effectively navigate
and address the prevailing challenges in the field.

综述中介绍了大规模语言建模在表格数据建模相关任务中的应用，包括预测、表格数据合成、问答和表格理解。通过归纳最新进展和总结数据集、度量标准和方法论，本综述发现了现有领域文献的优势、局限性、未开发的领域和研究间隙，并提供了相关代码和数据集的参考。期望以此为读者提供有关参考和深入见解，为他们在这个重要且快速进展的领域中有效应对挑战提供必备的工具和知识。

大型语言模型在表格数据上的应用 -- 综述

Large Language Models on Tabular Data -- A Survey

Recent work in large language modeling (LLMs) has used fine-tuning to align
outputs with the preferences of a prototypical user. This work assumes that
human preferences are static and homogeneous across individuals, so that
aligning to a a single "generic" user will confer more general alignment. Here,
we embrace the heterogeneity of human preferences to consider a different
challenge: how might a machine help people with diverse views find agreement?
We fine-tune a 70 billion parameter LLM to generate statements that maximize
the expected approval for a group of people with potentially diverse opinions.
Human participants provide written opinions on thousands of questions touching
on moral and political issues (e.g., "should we raise taxes on the rich?"), and
rate the LLM's generated candidate consensus statements for agreement and
quality. A reward model is then trained to predict individual preferences,
enabling it to quantify and rank consensus statements in terms of their appeal
to the overall group, defined according to different aggregation (social
welfare) functions. The model produces consensus statements that are preferred
by human users over those from prompted LLMs (>70%) and significantly
outperforms a tight fine-tuned baseline that lacks the final ranking step.
Further, our best model's consensus statements are preferred over the best
human-generated opinions (>65%). We find that when we silently constructed
consensus statements from only a subset of group members, those who were
excluded were more likely to dissent, revealing the sensitivity of the
consensus to individual contributions. These results highlight the potential to
use LLMs to help groups of humans align their values with one another.

通过 fine-tuning 机制，使用大型语言模型生成众人认可的共识性议论，以协调多样化的人类意见，并发现从子集中生成的共识性声明提高了个体贡献，并能够被人类用户所接受，具有较强的差异性和普适性。