Papers with Beam Search
SHARP: Search-Based Adversarial Attack for Structured Prediction (2022.findings-naacl)
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| Challenge: | SHARP is a new attack method for structured prediction models that solves several challenges. |
| Approach: | They propose a black-box adversarial attack method that uses a search-based optimization problem to attack adversarials. |
| Outcome: | The proposed method performs more potent attack than pioneer arts on two structured prediction tasks. |
Duplicate-Aware Controlled Code Generation: Enhancing Copyright Protection with Targeted Reordering Beam Search in LLMs (2026.findings-acl)
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| Challenge: | Experimental results demonstrate that TRBS effectively reduces verbatim repetition while maintaining functional adequacy. |
| Approach: | They propose a plug-and-play decoding method that dynamically reorders beam candidates to reduce direct copying. |
| Outcome: | The proposed method reduces verbatim repetition while maintaining functional adequacy on a multi-language code generation benchmark. |
Automatic Generation of Large-scale Multi-turn Dialogues from Reddit (2022.coling-1)
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| Challenge: | Using a set of algorithms, we can generate large dialogue corpus from Reddit. |
| Approach: | They propose to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. |
| Outcome: | The proposed methods improve on the baseline method by 36.3% . the best method shows an improvement of 36.6% over the previous one . |
Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) exhibit remarkable reasoning and planning capabilities, yet their substantial inference-time cost significantly impedes deployment in resourceconstrained applications. |
| Approach: | They propose a hybrid inference pipeline that combines beam search and Best-of-N . THROW generates shorter initial trajectories and evaluates them using PRMs . |
| Outcome: | THROW achieves 1.54 and 14.38 latency speedups and 35.7% and 80.4% token reductions on average compared to Best-of-N and beam search . |
ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations (2025.findings-acl)
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| Challenge: | Existing methods to train large language models do not capture how humans learn to think. |
| Approach: | They propose a method to fine-tune large language models for mathematical reasoning by using a text-infilling task that predicts masked equations from a given solution. |
| Outcome: | Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses baseline Masked Thought in performance and robustness with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. |
Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs’ Reasoning (2025.emnlp-main)
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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Yufei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Existing methods that use Chain-of-Thought suffer from path homogenization and inefficient use of intermediate results. |
| Approach: | They propose a framework that introduces checkpoints between reasoning steps to reduce path homogenization and create fault-tolerant mechanisms. |
| Outcome: | The proposed framework reduces path homogenization and creates fault-tolerant mechanism by utilizing high-quality intermediate results. |
Rectifying Belief Space via Unlearning to Harness LLMs’ Reasoning (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit sophisticated reasoning yet still generate incorrect answers. |
| Approach: | They propose a belief space rectification framework that suppresses spurious beliefs and enhances true ones to reduce erroneous reasoning and generalization. |
| Outcome: | The proposed framework reduces erroneous reasoning and improves generalization on three QA datasets and three LLMs. |