Enhance Multi-domain Sentiment Analysis of Review Texts through
Prompting Strategies
- URL: http://arxiv.org/abs/2309.02045v2
- Date: Sun, 7 Jan 2024 14:59:15 GMT
- Title: Enhance Multi-domain Sentiment Analysis of Review Texts through
Prompting Strategies
- Authors: Yajing Wang and Zongwei Luo
- Abstract summary: We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis.
We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies.
The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy.
- Score: 1.335032286337391
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have made significant strides in both scientific
research and practical applications. Existing studies have demonstrated the
state-of-the-art (SOTA) performance of LLMs in various natural language
processing tasks. However, the question of how to further enhance LLMs'
performance in specific task using prompting strategies remains a pivotal
concern. This paper explores the enhancement of LLMs' performance in sentiment
analysis through the application of prompting strategies. We formulate the
process of prompting for sentiment analysis tasks and introduce two novel
strategies tailored for sentiment analysis: RolePlaying (RP) prompting and
Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT
prompting strategy which is a combination of RP prompting and CoT prompting. We
conduct comparative experiments on three distinct domain datasets to evaluate
the effectiveness of the proposed sentiment analysis strategies. The results
demonstrate that the adoption of the proposed prompting strategies leads to a
increasing enhancement in sentiment analysis accuracy. Further, the CoT
prompting strategy exhibits a notable impact on implicit sentiment analysis,
with the RP-CoT prompting strategy delivering the most superior performance
among all strategies.
Related papers
- HPSS: Heuristic Prompting Strategy Search for LLM Evaluators [81.09765876000208]
We propose a novel automatic prompting strategy optimization method called Heuristic Prompting Strategy Search (HPSS)
Inspired by the genetic algorithm, HPSS conducts an iterative search to find well-behaved prompting strategies for evaluators.
Extensive experiments across four evaluation tasks demonstrate the effectiveness of HPSS.
arXiv Detail & Related papers (2025-02-18T16:46:47Z) - EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
We propose explicit policy optimization (EPO) for strategic reasoning.
EPO provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior.
Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment.
arXiv Detail & Related papers (2025-02-18T03:15:55Z) - Meta Reasoning for Large Language Models [58.87183757029041]
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs)
MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task.
We evaluate the effectiveness of MRP through comprehensive benchmarks.
arXiv Detail & Related papers (2024-06-17T16:14:11Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning [76.3114831562989]
It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments.
We propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)"
arXiv Detail & Related papers (2024-02-02T16:07:05Z) - StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving [76.5322280307861]
StrategyLLM allows LLMs to perform inductive reasoning, deriving general strategies from specific task instances, and deductive reasoning, applying these general strategies to particular task examples, for constructing generalizable and consistent few-shot prompts.
Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2% $rightarrow$ 38.8%), commonsense reasoning (70.3% $rightarrow$ 72.5%), algorithmic reasoning (73.7% $rightarrow$ 85.0
arXiv Detail & Related papers (2023-11-15T09:18:09Z) - Analyzing Different Expert-Opined Strategies to Enhance the Effect on
the Goal of a Multi-Attribute Decision-Making System Using a Concept of
Effort Propagation and Application in Enhancement of High School Students'
Performance [0.0]
This paper proposes two such strategies, namely parallel and hierarchical effort assignment, and propagation strategies.
The strategies are analyzed for a real-life case study regarding Indian high school administrative factors that play an important role in enhancing students' performance.
arXiv Detail & Related papers (2023-07-05T12:53:40Z) - Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A
Study on Prompt Design Strategies [20.15851744895469]
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks.
In this paper, we aim to extend this method to question answering tasks that utilize structured knowledge sources.
arXiv Detail & Related papers (2023-05-21T22:44:25Z)
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.