Papers with filters
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)
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| Challenge: | a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts . |
| Approach: | They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
| Outcome: | The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (P19-1)
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| Challenge: | Abstractive summarization models are limited in size and noisy training data. |
| Approach: | They propose a bi-directional selective encoding with template model which leverages template from training data to softly select key information from each source article to guide its summarization process. |
| Outcome: | The proposed model improves the summarization performance significantly on a standard summarizing dataset. |
SLoRA: Balancing Plasticity and Forgetting in Large Language Models for Continual Learning (2026.acl-long)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse tasks through large-scale pretraining. |
| Approach: | They propose a framework that filters noisy components from LoRA updates via subspace similarity with the base model. |
| Outcome: | The proposed framework improves accuracy by 12%, reduces forgetting by 29%, and filters out over 30% of LoRA parameters identified as noisy. |
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface (2020.lrec-1)
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| Challenge: | Mexico has 68 linguistic groups and 364 varieties, but lack of data on social media and internet is putting them at risk. |
| Approach: | They propose a collaborative corpus for endangered languages in Mexico . they propose linguistic search, digitalization and alignment process for each language . |
| Outcome: | The proposed corpus aligns Spanish with six indigenous languages: Maya, Ch’ol, Mazatec, Mixtec, Otomi, and Nahuatl. |
ALaRM: Align Language Models via Hierarchical Rewards Modeling (2024.findings-acl)
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| Challenge: | Current alignment approaches struggle with inconsistency and sparsity of human supervision signals. |
| Approach: | They propose a framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF) it integrates holistic rewards with aspect-specific rewards to enhance alignment of large language models with human preferences. |
| Outcome: | The proposed framework improves the alignment of large language models with human preferences by integrating holistic rewards with aspect-specific rewards. |
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve QA efficiency do not take specific answers into account. |
| Approach: | They propose a transformer-based approach to improve QA efficiency by filtering out questions that will not be answered by the system. |
| Outcome: | The proposed model can approximate the Precision/Recall curves of the target QA system. |
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (2023.findings-emnlp)
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| Challenge: | Large Language Models have made remarkable strides in various tasks, but whether they are competitive few-shot solvers remains an open question. |
| Approach: | They propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. |
| Outcome: | The proposed system achieves promising improvements on various IE tasks with acceptable time and cost investment. |
ToolWriter: Question Specific Tool Synthesis for Tabular Data (2023.emnlp-main)
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| Challenge: | Tabular question answering (TQA) requires joint reasoning of natural language with large amounts of semi-structured data. |
| Approach: | They propose to use query-specific programs to generate query-based tools to simplify large tables and detect when to apply them to transform tables. |
| Outcome: | The proposed tool improves state-of-the-art on two tabular question-answering datasets. |
MENTOR: Mitigating Identity Drift in Dynamic Role-Playing via Dual-Chain Structured Memory (2026.findings-acl)
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| Challenge: | Long-context LLMs are evolving into long-term agents that interact with users over extended horizons. |
| Approach: | They propose a cognitive architecture that mitigates identity drift without fine-tuning . MENTOR uses a Dual-Chain Memory Mechanism: a Global Chain (G) for long-term event logging and isolated Role Chains (Rr) as per-role working memories. |
| Outcome: | The proposed architecture improves the overall score (Avg) from 0.46 to 0.75 on average, with substantial gains in identity adherence and knowledge fidelity. |
TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026.acl-long)
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| Challenge: | Current reward models for reinforcement learning (RL) rely on outcome rewards that propagate a single scalar value across all tokens based on final correctness. |
| Approach: | They propose a framework that derives dense token-level supervision from LLMs . they use a multi-granularity calibration mechanism to modulate teacher influence . |
| Outcome: | The proposed framework evaluates teacher reliability across problem-level expertise, trajectory-level discrimination, and token-level confidence. |