Papers with inference framework
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning (2021.starsem-1)
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| Challenge: | Currently, symbolic and deep learning approaches to NLI are receiving less attention. |
| Approach: | They propose a symbolic-based inference framework that integrates symbolic reasoning and semantic formalism to solve NLI tasks. |
| Outcome: | The proposed framework improves accuracy on the NLI task and on the SICK and MED datasets. |
Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models (2023.findings-acl)
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| Challenge: | Existing methods to generate intermediate steps (CoT) are limited by the maximum context size due to various reasons. |
| Approach: | They propose a new inference framework that introduces several special tokens that the models can output to trigger context-related operations. |
| Outcome: | Extensive experiments with multiple architectures including GPT-3 show that the proposed framework significantly improves LMs’ inference capability. |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
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| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |
A Robust Dual-debiasing VQA Model based on Counterfactual Causal Effect (2024.findings-emnlp)
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| Challenge: | Existing VQA models suffer from language bias that indicates a spurious correlation between textual questions and answers. |
| Approach: | They propose a model agnostic dual-debiasing framework that models two types of language bias by separate branches under counterfactual inference framework. |
| Outcome: | The proposed framework significantly reduces language bias and achieves state-of-the-art performance on the benchmark datasets. |
SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have remarkable emergent abilities across various tasks, yet their performance on complex reasoning and planning tasks remains suboptimal. |
| Approach: | They propose a tree-search-based reasoning framework that encourages the exploration of intermediate steps and a round-scheduled strategy to manage draft model dispatching. |
| Outcome: | The proposed framework improves runtime speed and GPU memory management concurrently and handles multiple iterations for thought generation and state evaluation. |
Attend, Select and Eliminate: Accelerating Multi-turn Response Selection with Dual-attention-based Content Elimination (2023.findings-acl)
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| Challenge: | Pre-trained language models can be used to perform multi-turn response selection, but they can be expensive. |
| Approach: | They propose a framework and a strategy that progressively selects and eliminates unimportant content under context-response dual-attention. |
| Outcome: | The proposed method can effectively speed-up SOTA models without much performance degradation and shows a better trade-off between speed and performance than previous methods. |
Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)
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| Challenge: | Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels. |
| Approach: | They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts. |
| Outcome: | The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining. |
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)
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| Challenge: | Existing work mitigates memory overhead by offloading or compressing the Key-Value cache. |
| Approach: | They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method. |
| Outcome: | The proposed method outperforms the state-of-the-art in long-context evaluations. |
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding (2026.findings-acl)
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| Challenge: | Multimodal large language models generate medical hallucinations due to over-sensitivity to clinical sections. |
| Approach: | They propose a framework that integrates structured clinical signals from task-specific radiology expert models. |
| Outcome: | The proposed framework improves overall performance on radiology report generation (RRG) on the MIMIC-CXR dataset, it yields up to 17% improvement in RadGraph-F1. |