The Power of Bullet Lists: A Simple Yet Effective Prompting Approach to Enhancing Spatial Reasoning in Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are currently dominating the field of natural language processing, but spatial reasoning ability is lacking in LLMs. |
| Approach: | They propose a prompting technique that integrates bullet lists, coordinates, and visualizations into the reasoning process and integrates them into planning tasks. |
| Outcome: | The proposed technique boosts LLMs' spatial reasoning abilities compared to previous prompting techniques. |
Similar Papers
TORSO: Template-Oriented Reasoning Towards General Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate responses using few-shot examples depend on the provided examples, limiting the model’s reasoning capabilities. |
| Approach: | They propose a model that emulates human reasoning during response generation by using curated few-shot prompts instead of manually crafted few-shot examples. |
| Outcome: | The proposed model achieves strong performance on diverse LLMs benchmarks with reasonable rationales. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
Copied to clipboard
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Visual Prompting in LLMs for Enhancing Emotion Recognition (2024.emnlp-main)
Copied to clipboard
Qixuan Zhang, Zhifeng Wang, Dylan Zhang, Wenjia Niu, Sabrina Caldwell, Tom Gedeon, Yang Liu, Zhenyue Qin
| Challenge: | Existing methods for enhancing in-context emotion classification fail to include spatial relationships between different people and facial features within a single face. |
| Approach: | They propose a set-of-vision prompting approach that uses spatial information to mark targets precisely. |
| Outcome: | The proposed approach improves face count and emotion categorization while preserving the enriched image context. |
“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a decoding strategy that nudges LLMs to continue reasoning, thereby preventing immature reasoning processes. |
| Outcome: | The proposed method significantly improves LLM reasoning capabilities on diverse reasoning benchmarks. |
Batch Prompting: Efficient Inference with Large Language Model APIs (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Performing inference on large volumes of samples can be computationally and financially costly. |
| Approach: | They propose a prompting approach that enables large language models to run inference in batches instead of one sample at a time. |
| Outcome: | The proposed prompting reduces both token and time costs while retaining downstream performance. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. |
| Approach: | They propose an approach that optimizes the zero-shot prompts for individual task instances following an innovative manner of "LLMs in the loop" their results show that PRomPTed outperforms naive zero- shot approaches and a strong baseline which refines the task output instead of the input prompt. |
| Outcome: | The proposed approach outperforms naive approaches and a strong baseline which refines the task output instead of the input prompt. |
Dissecting Clinical Reasoning in Natural Language Inference for Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. |
| Approach: | They examine four classes of prompting strategies to elicit reasoning in large language models . they then construct demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models based on Low-Rank Adaptation (LoRA). |
| Outcome: | The proposed model improves in 75% of the models on MedNLI and TREC Clinical Trials. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |