BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detection

• URL: http://arxiv.org/abs/2604.11121v1
• Date: Mon, 13 Apr 2026 07:35:17 GMT
• Title: BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detection
• Abstract summary: We present a two-stage approach for detecting political polarization in social media text.<n>We fine-tune Qwen 2.5-7B-Instruct with LoRA using an interpretable slot-filling template.<n>Experiments on the SemEval 2026 POLAR shared task dataset show that preference-based refinement improves both accuracy and decreases false negatives without extra annotation.
• Score: 2.2588605422113606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The POLAR SemEval-2026 Shared Task aims to detect online polarization and focuses on the classification and identification of multilingual, multicultural, and multi-event polarization. Accurate computational detection of online polarization is challenging due to nuanced rhetoric, implicit framing, and the high cost of human-in-the-loop annotation. Building on recent findings that contextual prompting enables large language models to function as strong polarization detectors, we present a two-stage approach for detecting political polarization in social media text that combines structured supervised fine-tuning with Direct Preference Optimization (DPO) refinement. We fine-tune Qwen 2.5-7B-Instruct with LoRA using an interpretable slot-filling template (target, claim type, manifestation checklist, and justification). We then apply DPO with automatically generated preference pairs to reduce costly false negatives. Experiments on the SemEval 2026 POLAR shared task dataset show that preference-based refinement improves both accuracy and decreases false negatives without extra annotation. On the English development set, DPO increases recall from 0.5085 to 0.7797 and improves macro-F1 by ~5 points.

Related papers

• Combating Instruction Conflict via Energy-Driven Latent Conflict Detection [6.701569487024643]
  Large Language Models (LLMs) are increasingly deployed with hierarchical instructions, yet they remain vulnerable to conflicts in which user directives override system-level constraints.<n>We introduce ELCD, a response-level latent conflict detector for post-generation, pre-delivery verification.
  arXiv  Detail & Related papers  (2026-09-08T12:14:43Z)
• Stokes-Informed Diffusion for Robust Linear Polarization Estimation [49.459667092743445]
  GenPolar is a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation.<n>It achieves state-of-the-art performance in both DoLP fidelity and AoP stability.<n>These improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
  arXiv  Detail & Related papers  (2026-07-23T11:56:15Z)
• Polarization Detection: A Hybrid Approach with AfroXLMR-Social and DeBERTa for Low- and High-Resource Settings [0.0]
  This paper presents our system description for the POLAR Task 2026, focusing on the detection and characterization of polarized discourse in English and Hausa.<n>We propose a hybrid modeling strategy for English binary detection, we leverage the monolingual strength of textbfDeBERTa.<n>We report competitive results across all three subtasks, demonstrating that model selection tailored to specific subtask requirements yields the best balance of performance.
  arXiv  Detail & Related papers  (2026-07-11T13:37:55Z)
• Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems [72.27018654934386]
  Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences.<n>In recommender systems, however, user feedback is rarely pairwise.<n>We propose Mult-DPO, a tractable multinomial surrogate likelihood over set-wise preference events for the user-preference alignment of LLM-based recommender systems.
  arXiv  Detail & Related papers  (2026-06-08T18:53:45Z)
• MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization [66.81594613265833]
  Multi-negative preference optimization under the Plackett--Luce (PL) model extends Direct Preference Optimization (DPO)<n>We introduce MASS-DPO, a multi-negative active sample selection method that derives a PL-specific Fisher-information objective for selecting compact, informative negative subsets within each prompt.<n> MASS-DPO consistently exceeds or matches existing methods in accuracy, improves Recall/NDCG and margin-based optimization dynamics, and delivers stronger alignment with substantially fewer negatives.
  arXiv  Detail & Related papers  (2026-05-11T16:18:08Z)
• mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection [5.3677118215189585]
  SemEval-2026 Task 9 is focused on multilingual polarization detection.<n>It covers the identification of multilingual, multicultural and multievent polarization along three axes, namely detection, type, and manifestation.<n>We have coped with this SemEval task by finetuning mid-size LLMs for the sequence-classification task using the QLoRA parameter-efficient finetuning technique.<n>The training data augmented the multilingual (22 languages) training sets by anonymized, lower-cased, upper-cased, and homoglyphied counterparts, making the detection more robust.
  arXiv  Detail & Related papers  (2026-05-04T15:08:24Z)
• Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE [7.609008983716641]
  We conduct experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO training.<n>Our central finding is that a simple modification, replacing deterministic hard negatives with sampling from a dynamic top-K candidate pool, consistently improves ranking performance.
  arXiv  Detail & Related papers  (2026-03-31T04:49:32Z)
• Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings [0.0]
  This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text.<n>I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification.
  arXiv  Detail & Related papers  (2026-03-08T15:50:37Z)
• Uncertainty-Penalized Direct Preference Optimization [52.387088396044206]
  We develop a pessimistic framework for DPO by introducing preference uncertainty penalization schemes. The penalization serves as a correction to the loss which attenuates the loss gradient for uncertain samples. We show improved overall performance compared to vanilla DPO, as well as better completions on prompts from high-uncertainty chosen/rejected responses.
  arXiv  Detail & Related papers  (2024-10-26T14:24:37Z)
• Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level [50.897438358317686]
  We show that iLR-DPO can enhance a 7B model to perform on par with GPT-4 without increasing verbosity. Specifically, our 7B model achieves a $50.5%$ length-controlled win rate against $texttGPT-4 Preview$ on AlpacaEval 2.0.
  arXiv  Detail & Related papers  (2024-06-17T17:55:38Z)
• Self-Play Preference Optimization for Language Model Alignment [75.83359213697854]
  Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences. We propose a self-play-based method for language model alignment, which treats the problem as a constant-sum two-player game. Our approach, dubbed Self-Play Preference Optimization (SPPO), utilizes iterative policy updates to provably approximate the Nash equilibrium.
  arXiv  Detail & Related papers  (2024-05-01T17:59:20Z)

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.