Source-linked AI summary
Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection
Yifan Xiang, Bin Liang, Yuqi Huang, Ruifeng Xu, Kam-Fai Wong
TL;DR
Conversational stance detection requires using target-related history without allowing irrelevant or misleading turns to add noise. TamGraph selectively retrieves historical memories through entropy-guided backtracking, dynamically builds a target-aware stance graph, and substantially improves LLM performance on English and Chinese benchmarks. Its evaluation is limited by reliance on backbone LLM capabilities, overhead on long conversations, and testing mainly on comparatively small models.
Problem
Existing conversational stance methods rely roughly on full conversation history, which can include target-irrelevant or misleading content that hinders stance detection.
Method
TamGraph uses stepwise, entropy-guided backward retrieval to activate target-related propositions and incrementally construct a graph encoding relations among them for LLM-based stance detection.
Results
TamGraph consistently improves performance over prompting baselines and ablation variants across LLM backbones on the English MT-CSD and Chinese ZS-CSD benchmarks.
Takeaways & Limitations
TamGraph provides target-centric contexts by retaining confidence-improving evidence and filtering misleading or irrelevant conversation history.
Takeaways & Limitations
The method depends on backbone LLM capabilities, can add latency and token consumption for long conversations, and has mainly been evaluated on comparatively small models.
Abstract
from arXiv · showhide
Stance detection is crucial for understanding the underlying attitude of an expression towards a target. Conversational stance detection is a more challenging stance detection task in real-world social media scenarios, as it involves detecting the user's stance by leveraging the target-related historical statements across conversational sessions. In this paper, we propose target-aware Memory Graph TamGraph, a novel method that dynamically leverages target-related statements for conversational stance detection. Instead of considering all preceding historical conversations or using no prior conversation information for stance detection, our TamGraph employs a stepwise, entropy-guided backtracking mechanism to selectively activate memory from historical conversations and dynamically constructs a target-aware graph to model the stance relations among utterances. This allows the exploitation of target-related information from the conversation history for stance detection while preventing the introduction of noise. Experimental results on both English and Chinese benchmarks demonstrate that our TamGraph substantially improves LLM performance on conversational stance detection.
1 Introduction
Conversational stance detection must interpret utterances within target-related dialogue history, but full-history use can introduce irrelevant or misleading information. TamGraph selectively retrieves useful memories and builds a target-aware graph to support stance prediction.
- Conversational stance detection determines an utterance’s favor, against, or neutral stance toward a target using conversation information.
- LLM stance-detection approaches commonly focus on single utterances, missing the conversational embedding of utterances in real-world social-media threads.
- Using full conversation history can introduce target-irrelevant or misleading content that adds noise and hinders stance detection.
- TamGraph uses stepwise, entropy-guided backtracking to activate target-related historical information and dynamically construct a graph of stance relations among utterances.
- The resulting target-focused, pruned context filters irrelevant and misleading information for more reliable stance detection in multi-turn interactions.
- Across LLM backbones, TamGraph selectively encodes target-related memories and improves performance over prompting baselines and ablation variants.
2 Related Works
Prior work has advanced LLM-based stance detection through prompting, fine-tuning, and bias analysis, while conversational benchmarks model stance in multi-turn settings. This paper extends the area by focusing on conversational rather than single-utterance stance detection.
- Prompting strategies use reasoning chains to elicit incremental, interpretable inference for stance detection.
- Bias-oriented analyses examine LLM behaviour across diverse settings and offer mitigation directions.
- Data augmentation with fine-tuning has demonstrated feasibility for providing task-specific knowledge in stance detection.
- Existing LLM efforts primarily address single-utterance stance detection without multi-turn context, whereas this research targets conversational settings.
- GLAN and ZS-CSD introduce English and Chinese conversational stance benchmarks with methods for modeling reply dependencies, local interactions, or speaker interactions.
3 Methods
TamGraph incrementally builds a target-aware memory graph for conversational stance detection. It selectively retains historical information when it lowers predictive uncertainty, discarding updates that do not improve confidence or stopping when confidence is sufficient.
- 3.1 TamGraph Initialization: TamGraph initializes a graph with the final utterance, prompts an LLM for an initial stance prediction, and computes its probability distribution and entropy.The initial graph contains only the final utterance and no edges.
- 3.2 TamGraph Updates and Inference Procedure: If initial entropy exceeds the threshold, TamGraph traverses the reply chain backward and extracts propositions related to the target from preceding utterances.The retrieved propositions become candidate memories for graph expansion.
- 3.2 TamGraph Updates and Inference Procedure: After each graph update, TamGraph recomputes the LLM prediction and retains the update only when the new entropy is no greater than the retained prediction’s entropy.Otherwise, it discards the candidate utterance and continues to the next preceding utterance.
- 3.2 TamGraph Updates and Inference Procedure: The procedure stops when entropy falls below the threshold or no preceding utterances remain, returning the prediction from the last retained graph update.The detailed algorithm and prompt specifications are provided in the appendix.
- 3.2 TamGraph Updates and Inference Procedure: For each candidate memory, TamGraph induces relations such as support, against, or questions between propositions from the new and retained utterances.These relations are represented as graph edges, while propositions serve as graph nodes.
4 Experimental Setup
The experiments evaluate TamGraph on English and Chinese conversational stance benchmarks using multiple LLMs. Performance is measured with accuracy and macro F1 under reproducible zero-temperature prompting.
- Benchmarks: MT-CSD contains 15876 English instances targeting Bitcoin, Tesla, SpaceX, Biden, and Trump.Its labels are favor, against, or none.
- Benchmarks: ZS-CSD consists of 17063 Chinese conversations with 113 noun-phrase-type targets and 167 claim-type targets.Its labels are favor, against, or none.
- Models: The study evaluates five LLMs across model sizes, including Qwen2.5-Instruct variants of 3B, 7B, and 14B.The supplied passage begins listing the evaluated models but does not include the complete list.
- Implementation Details: Model performance is evaluated using accuracy and macro F1, with temperature set to 0 to support stable and reproducible predictions.English prompts are used for MT-CSD and Chinese translations for ZS-CSD.
5 Results
TamGraph improves conversational stance detection by selectively retrieving target-related memories, constructing a dynamic stance graph, and filtering irrelevant history. Across English and Chinese benchmarks, it outperforms prompting, retrieval, and non-LLM baselines while reducing inference costs.
- Main Results: TamGraph achieves the highest overall weighted accuracy and macro F1 across all targets and both benchmarks.Averaged improvements over single-turn, multi-turn, and CoT baselines are 4.93%, 5.68%, and 2.75% in weighted accuracy, and 6.68%, 5.56%, and 1.82% in macro F1.
- Main Results: Across 29 groups, TamGraph ranks first in 19 and second in 5, with particularly strong gains on Tesla and Trump.The method also performs well where multi-turn input often degrades performance, including Tesla and SpaceX.
- Main Results: In noise-heavy Biden settings, TamGraph ranks first in 6 of 9 groups and second in 1 group despite 60.89% and 56.51% irrelevant-utterance rates.These results support selective memory activation when conversation history contains substantial irrelevant or potentially harmful context.
- Main Results: TamGraph outperforms non-LLM baselines and reaches 46.59% macro-F1 on ZS-CSD with Qwen2.5-14B.This exceeds the 43.81% reported by SITPCL; gains are especially substantial on smaller Qwen backbones, while Llama-3.1-8B improvements are more moderate.
- Retrieval-based Baselines: Compared with BM25 retrieval, TamGraph consistently performs better across models and query settings.The comparison indicates that its gains depend on entropy-guided evidence selection and structured evidence integration, not merely retrieving topical turns.
- Ablation Studies: Entropy-guided early stopping reduces token usage as the threshold increases while preserving competitive performance.Removing the entropy threshold causes a 3.60% accuracy drop and 1.46% macro F1 drop; random turn selection causes drops of 3.09% and 1.32%.
- Robustness Analysis: TamGraph remains effective despite possible hallucinations or extraction errors in LLM-generated intermediate outputs.The authors report that these outputs remain effective in aggregate for organizing target-related evidence.
- Qualitative Analysis: A qualitative example shows TamGraph correctly captures negative and sarcastic meaning where the CoT baseline predicts a positive stance.TamGraph identifies target-related propositions, induces their relations, and constructs a target-aware memory graph.
6 Conclusion
The paper finds that indiscriminate use of conversation history can impede LLM stance detection, motivating TamGraph’s selective, entropy-guided memory construction. Experiments on English and Chinese benchmarks show substantial performance improvements and support the proposed selective mechanisms.
- Full conversation history can impede LLM performance by introducing noise and target-irrelevant information.
- TamGraph selectively activates target-related propositions from preceding turns and dynamically updates a target-aware memory graph.Its procedure uses stepwise backtracking, entropy-guided memory updates, and early stopping.
- TamGraph substantially improves LLM performance on English MT-CSD and Chinese ZS-CSD conversational stance detection benchmarks.It also outperforms benchmark-specific non-LLM baselines.
- Ablation studies support the effectiveness of entropy-guided selective memory updating and early stopping.
Limitations
The method has limitations related to dependence on backbone-model capabilities, computational overhead, and evaluation on comparatively small models. These constraints affect reliability, efficiency, and the scope of scaling validation.
- TamGraph depends on backbone LLM capabilities for memory activation and entropy-based control, so intermediate errors may affect final results.
- Long conversations can impose substantial overhead because the stepwise procedure may require multiple iterations, increasing latency and token consumption.
- Computational constraints limited evaluation to comparatively small models, leaving validation on larger and more diverse frontier models for future work.
Ethical considerations
The study uses publicly available datasets under permissive licenses without personally identifiable information or human-subject data collection. However, the conversations may contain controversial or sensitive opinions, and model outputs can be incorrect.
- The datasets are publicly available, released under permissive licenses, and used consistently with their intended purposes.
- The datasets do not contain personally identifiable information, and no human subjects were involved in data collection.
- Conversations may include controversial or sensitive opinions about real-world entities such as public figures, organizations, or events.
- The method may produce incorrect predictions, and reported outputs reflect dataset content rather than the authors’ views.
C Target-irrelevant History Analysis.
The analysis measures target-irrelevant conversational content and compares full multi-turn prompting with single-turn prompting. The results show that additional context is not uniformly beneficial, supporting selective activation of target-related history.
- The study quantifies target-irrelevant utterances across all MT-CSD targets and the complete ZS-CSD benchmark.It reports proportions among all utterances and among history turns excluding the final utterance.
- A substantial proportion of conversational history is unrelated to the target, particularly in the Biden subset and ZS-CSD.
- Full multi-turn prompting performs worse than single-turn prompting in 14 of 29 accuracy settings and 12 of 29 macro-F1 settings.
- These comparisons show that additional conversational context is not uniformly beneficial and motivate selective activation of target-related history.
D Additional Ablation study
The additional ablation and case-study analyses examine entropy-threshold settings, qualitative baseline failures, and a shared error involving information-seeking questions with negative presuppositions.
- Entropy-threshold ablation: Entropy thresholds of 0.0, 0.2, 0.4, and 0.8 receive separate ablation results reporting accuracy and macro F1.The 0.6 setting is reported in Table 1.
- Qualitative comparison: In one qualitative example, the CoT baseline produces garbled output and fails to yield a valid prediction.TamGraph's examples also illustrate entropy-guided conversation-turn selection.
- Qualitative comparison: In another qualitative example, CoT incorrectly predicts a favor stance after being impacted by the overall conversational stance.The example contrasts this behavior with the TamGraph visualization of selected memories.
- Failure case: All baselines and TamGraph predict against instead of none for a question about a simpler, less error-prone Tesla adaptive-cruise-control option.The error concerns distinguishing an information-seeking question with negative presuppositions from an explicit negative stance.
G Inference Efficiency Analysis
The inference-efficiency analysis evaluates backtracking depth, token usage, and end-to-end latency, finding that TamGraph usually stops early and is faster than CoT while reducing token usage.
- Backtracking depth: 83.35% of test cases terminate after one additional backtracking step, while 97.28% terminate within two additional steps.Only 2.72% of cases require three or more additional backtracking steps.
- Backtracking depth: Entropy-based early stopping controls the added inference cost by terminating backtracking after sufficient evidence is collected.The reported depth distribution indicates that backtracking typically stops early.
- Token efficiency: TamGraph substantially reduces token usage compared with the baselines in the token-level results shown in Figure 4.The analysis presents this reduction as evidence of framework cost efficiency.
- Runtime latency: TamGraph is faster than CoT on both benchmarks, achieving an overall speedup of approximately 1.22× under matched models, datasets, and hardware.The latency measurement includes proposition extraction, relation induction, memory construction, and final stance prediction.