We are delighted to announce that four papers from the Intelligent Information Processing Lab (IIPL, Prof. YoungBin Kim) have been accepted to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026).

[ACL 2026 -Main Conference-]

Title:
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering

Authors:
JungMin Yun, YoungBin Kim

Abstract:
Multi-hop question answering requires complex reasoning over multiple evidence segments, often overwhelming retrieval-augmented generation systems with lengthy and noisy contexts that undermine both efficiency and accuracy. While existing prompt compression methods mitigate this issue, they are typically designed for single-turn queries and fail to capture interdependent reasoning steps. We propose IterCOMP, a training-free unified prompt compression framework that embeds multi-hop reasoning within an iterative compression loop. IterCOMP decomposes documents into evidence segments, evaluates questions answerability, and generates targeted follow-up questions to iteratively integrate essential evidence, producing a compact reasoning-oriented prompt. Experiments on MusiQue, 2WikiMultiHopQA, and HotpotQA demonstrate that IterCOMP achieves substantial improvements in Exact Match and F1 while reducing token budget, outperforming existing baselines and showing robustness under increasing reasoning complexity.

Title:
FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation

Authors:
Jinhee Jang, Juhwan Choi, Dongjin Lee, Seunguk Yu, YoungBin Kim

Abstract:
Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender bias. In particular, they tend to favor masculine realizations in gender-ambiguous contexts and may assign higher scores to gender-misaligned translations even when gender is explicitly specified. To address these issues, we propose FairQE, a multi-agent-based, fairness-aware QE framework that mitigates gender bias in both gender-ambiguous and gender-explicit scenarios. FairQE detects gender cues, generates gender-flipped translation variants, and combines conventional QE scores with LLM-based unbiased reasoning through a dynamic bias-aware aggregation mechanism. This design preserves the strengths of existing QE models while calibrating their gender-related biases in a plug-and-play manner. Extensive experiments across multiple gender bias evaluation settings demonstrate that FairQE consistently improves gender fairness over strong QE baselines. Moreover, under MQM-based meta-evaluation following the WMT 2023 Metrics Shared Task, FairQE achieves competitive or improved general QE performance. These results show that gender bias in QE can be effectively mitigated without sacrificing evaluation accuracy, enabling fairer and more reliable translation evaluation.

[ACL 2026 -Findings-]

Title:
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks

Authors:
Junehyoung Kwon, MiHyeon Kim, Eunju Lee, JungMin Yun, Byeonggeuk Lim, YoungBin Kim

Abstract:
While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning benchmarks attempt to mitigate this using fictitious identities but overlook a critical textit{stage 1 failure}: models fail to effectively memorize target information initially, rendering subsequent unlearning evaluations unreliable. Diagnosing under-memorization and the multi-hop curse as root causes, we introduce ReMem, a Reliable Multi-hop and Multi-image Memorization Benchmark. ReMem ensures robust foundational learning through principled data scaling, reasoning-aware QA pairs, and diverse visual contexts. Additionally, we propose a novel Exposure metric to quantify the depth of information erasure from the model's internal probability distribution. Extensive experiments demonstrate that ReMem provides a rigorous and trustworthy framework for diagnosing both learning and unlearning behaviors in LVLMs.

Title:
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs

Authors:
Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon, Kyeonghyun Kim, YoungBin Kim

Abstract:
Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference datasets. We identify that this reliance introduces a distributional mismatch between the proprietary and target models that hinders efficient alignment. To address this, we propose Alignment via VErified Self-correction DPO (AVES-DPO), a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. Our approach employs a consensus-based verification mechanism to diagnose diverse hallucinations and guides the model to self-correct, thereby generating preference pairs strictly compatible with its internal distribution. Extensive experiments demonstrate that AVES-DPO surpasses existing baselines in hallucination mitigation while requiring only 5.2k samples.