A Logically Consistent Chain-of-Thought Approach for Stance Detection
- URL: http://arxiv.org/abs/2312.16054v1
- Date: Tue, 26 Dec 2023 13:54:00 GMT
- Title: A Logically Consistent Chain-of-Thought Approach for Stance Detection
- Authors: Bowen Zhang, Daijun Ding, Liwen Jing and Hu Huang
- Abstract summary: Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets.
We introduce a novel approach named Logically Consistent Chain-of-Thought (LC-CoT) for ZSSD.
LC-CoT improves stance detection by ensuring relevant and logically sound knowledge extraction.
- Score: 4.895189262775054
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Zero-shot stance detection (ZSSD) aims to detect stances toward unseen
targets. Incorporating background knowledge to enhance transferability between
seen and unseen targets constitutes the primary approach of ZSSD. However,
these methods often struggle with a knowledge-task disconnect and lack logical
consistency in their predictions. To address these issues, we introduce a novel
approach named Logically Consistent Chain-of-Thought (LC-CoT) for ZSSD, which
improves stance detection by ensuring relevant and logically sound knowledge
extraction. LC-CoT employs a three-step process. Initially, it assesses whether
supplementary external knowledge is necessary. Subsequently, it uses API calls
to retrieve this knowledge, which can be processed by a separate LLM. Finally,
a manual exemplar guides the LLM to infer stance categories, using an if-then
logical structure to maintain relevance and logical coherence. This structured
approach to eliciting background knowledge enhances the model's capability,
outperforming traditional supervised methods without relying on labeled data.
Related papers
- A Survey of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment [6.492836595169771]
Event Causality Identification (ECI) has become a crucial task in Natural Language Processing (NLP)
Our taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification.
arXiv Detail & Related papers (2024-11-15T17:19:42Z) - Rethinking State Disentanglement in Causal Reinforcement Learning [78.12976579620165]
Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability.
We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.
We propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation.
arXiv Detail & Related papers (2024-08-24T06:49:13Z) - Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making.
Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations.
Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
arXiv Detail & Related papers (2024-08-17T11:49:53Z) - Chain of Stance: Stance Detection with Large Language Models [3.528201746844624]
Stance detection is an active task in natural language processing (NLP)
We propose a new prompting method, called textitChain of Stance (CoS)
arXiv Detail & Related papers (2024-08-03T16:30:51Z) - Cross-target Stance Detection by Exploiting Target Analytical
Perspectives [22.320628580895164]
Cross-target stance detection (CTSD) is an important task, which infers the attitude of the destination target by utilizing annotated data from the source target.
One important approach in CTSD is to extract domain-invariant features to bridge the knowledge gap between multiple targets.
We propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge.
arXiv Detail & Related papers (2024-01-03T14:28:55Z) - Exploiting Low-confidence Pseudo-labels for Source-free Object Detection [54.98300313452037]
Source-free object detection (SFOD) aims to adapt a source-trained detector to an unlabeled target domain without access to the labeled source data.
Current SFOD methods utilize a threshold-based pseudo-label approach in the adaptation phase.
We propose a new approach to take full advantage of pseudo-labels by introducing high and low confidence thresholds.
arXiv Detail & Related papers (2023-10-19T12:59:55Z) - Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection [73.31406286956535]
We introduce the Ladder-of-Thought (LoT) for the stance detection task.
LoT directs the small LMs to assimilate high-quality external knowledge, refining the intermediate rationales produced.
Our empirical evaluations underscore LoT's efficacy, marking a 16% improvement over GPT-3.5 and a 10% enhancement compared to GPT-3.5 with CoT on stance detection task.
arXiv Detail & Related papers (2023-08-31T14:31:48Z) - Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual
Recognition [57.08108545219043]
Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision.
Existing literature addresses this challenge by employing local-based representation approaches.
This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
arXiv Detail & Related papers (2023-05-12T00:13:17Z) - Reason from Context with Self-supervised Learning [15.16197896174348]
We propose a new Self-supervised method with external memories for Context Reasoning (SeCo)
In both tasks, SeCo outperformed all state-of-the-art (SOTA) SSL methods by a significant margin.
Our results demonstrate that SeCo exhibits human-like behaviors.
arXiv Detail & Related papers (2022-11-23T10:02:05Z) - MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning [63.50909998372667]
We propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text.
Two novel strategies serve as indispensable components of our method.
arXiv Detail & Related papers (2022-03-01T11:13:00Z)
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.