Papers with prompting methods
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)
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| Challenge: | Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type . |
| Approach: | They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting . |
| Outcome: | The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance. |
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)
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| Challenge: | Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data. |
| Approach: | They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step . |
| Outcome: | The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance. |
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
| Approach: | They introduce an adversarial grade school math dataset and explore whether LLMs can be more robust when questions are slightly changed. |
| Outcome: | The proposed method generates and verifies each intermediate thought based on its reasoning goal and calculation result. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Iteratively Prompt Pre-trained Language Models for Chain of Thought (2022.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) internalize a great amount of knowledge, but have been shown incapable of recalling this knowledge to solve complex & multi-step reasoning tasks. |
| Approach: | They propose an iterative prompting framework which progressively elicits relevant knowledge from PLMs for multi-step inference. |
| Outcome: | The proposed prompting framework outperforms existing prompting methods on three datasets involving multi-step reasoning. |
DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025.findings-naacl)
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| Challenge: | Automatic radiology report generation is challenging due to inherent biases in medical imaging data. |
| Approach: | They propose a disease description graph that encapsulates comprehensive and pertinent disease information. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets . the proposed model is based on a three-layer decoder and improves on existing models . |
Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning (2025.naacl-long)
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| Challenge: | Existing studies have focused on text-based cognitive reframing, but neglected the importance of non-verbal evidence in real-life therapy. |
| Approach: | They propose a dataset that pairs each GPT-4-generated dialogue with an image that reflects the virtual client’s facial expressions to better mirror real psychotherapy, where facial expression leads to interpreting implicit emotional evidence. |
| Outcome: | The proposed approach outperforms existing methods with LLMs and vision-language models and provides more thoughtful and empathetic suggestions. |
Robust Native Language Identification through Agentic Decomposition (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) often achieve high performance by leveraging superficial contextual clues rather than the underlying linguistic patterns indicative of native language (L1) influence. |
| Approach: | They propose an agentic NLI pipeline where specialized agents accumulate and categorize diverse linguistic evidence before an independent final assessment. |
| Outcome: | The proposed pipeline significantly improves robustness against misleading contextual clues and performance consistency compared to standard prompting methods. |
Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)
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| Challenge: | Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage. |
| Approach: | They propose a method to retrieve in-context principles from mistakes to improve model performance. |
| Outcome: | The proposed framework enhances model performance when applied to various prompting strategies. |
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting (2024.findings-acl)
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| Challenge: | Existing prompting methods oversimplify time-series forecasting (TSF) time-Series data are ubiquitous across various domains, including public health, finance and energy. |
| Approach: | They propose a method for prompting off-the-shelf Large Language Models (LLMs) they decompose TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each . |
| Outcome: | The proposed approach decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. |
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models (2023.findings-acl)
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Yuhui Zhang, Michihiro Yasunaga, Zhengping Zhou, Jeff Z. HaoChen, James Zou, Percy Liang, Serena Yeung
| Challenge: | Recent studies show that some tasks exhibit inverse scaling, or U-shaped scaling, where the performance degrades as models are scaled up. |
| Approach: | They propose a task that asks questions with negation to show positive scaling . they hypothesize that solving NeQA depends on question answering and negation understanding . |
| Outcome: | The proposed task can exhibit inverse scaling, U-shaped scaling, or positive scaling, and the scaling trends shift as the task is more powerful. |
A Sentiment Consolidation Framework for Meta-Review Generation (2024.acl-long)
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| Challenge: | Recent advances in abstractive text summarization have created plausible summaries, but it is unclear if they truly possess the capability of information consolidation to generate summary. |
| Approach: | They propose to prompt large language models to generate meta-reviews and use evaluation metrics to assess the quality of generated meta- reviews. |
| Outcome: | The proposed framework proves that human meta-reviewers follow a framework of sentiment consolidation to write meta- reviews compared with prompting them with simple instructions. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |
Empowering Multi-step Reasoning across Languages via Program-Aided Language Models (2024.emnlp-main)
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| Challenge: | In-context learning methods elicit Large Language Models to solve tasks using provided demonstrations without parameter updates. |
| Approach: | They propose a method for aligning reasoning programs across languages using a double-step cross-lingual prompting mechanism. |
| Outcome: | The proposed method outperforms existing prompting methods and reduces interaction time. |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
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| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)
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| Challenge: | Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. |
| Approach: | They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue. |
| Outcome: | The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context. |
LLM-Symbolic Integration for Robust Temporal Tabular Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for temporal tabular question answering are inconsistent and fail to provide the variability needed to thoroughly evaluate models. |
| Approach: | TEMPTABQA-C uses a synthetic dataset and symbolic representation to generate and execute SQL queries. |
| Outcome: | TEMPTABQA-C improves on previous methods for temporal tabular question answering . incorporating adaptive fewshot prompting with tailored examples improves performance . lack of robustness, scalability, and interpretable solutions is key obstacle . |
PICTURE: Enhancing Theory-of-Mind in Large Language Models by Revealing, Not Hiding, Characters’ Lack of Knowledge (2026.acl-long)
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| Challenge: | Existing approaches to simulating Theory of Mind (ToM) using LLMs have been a longstanding problem in natural language processing (NLP). |
| Approach: | They propose a new method that enables LLMs to generate a character’s lack of knowledge within free-form Chain-of-Thought (CoT) based on this method, they propose to generate perspective-taking outputs as free- form explanations without event hiding. |
| Outcome: | The proposed method outperforms existing prompting methods by an average of 7.3% on false-belief tasks. |