Papers with arithmetic reasoning
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for low-rank averaging of LoRA adapters result in inexact updates. |
| Approach: | They propose a method which adds a residual error term to the pre-trained frozen weight matrix to achieve exact updates with minimal computational and communication overhead. |
| Outcome: | The proposed method achieves exact updates with minimal computational and communication overhead, preserving LoRA’s efficiency. |
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. |
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in scale have enabled large language models to perform NLP tasks zero-shot . however, it is not known whether ChatGPT can serve as a generalist model that can perform many NLP jobs zero- shot. |
| Approach: | They empirically evaluate ChatGPT's zero-shot learning ability on 20 popular NLP datasets . they find it performs well on many tasks favoring reasoning abilities . |
| Outcome: | The proposed model can perform many NLP tasks zero-shot without adaptation on downstream data. |
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. |
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models excel in various tasks, but their huge size and inaccessibility of parameters present challenges for practical deployment. |
| Approach: | They propose to use CoT data to distill task-specific ability from large language models to smaller models . they use reasoning programs to suppress errors in distilled data and improve distillation quality . |
| Outcome: | The proposed model outperforms LLMs on arithmetic reasoning, symbolic reasoning, and general ability. |
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Representation Fine-Tuning (ReFT) adapts large pre-trained models by updating only a small subset of parameters. |
| Approach: | They propose a method that uses sparse intervention layers to steer hidden representations directly to capture rich semantic information. |
| Outcome: | The proposed approach outperforms PEFTs on commonsense reasoning, arithmetic reasoning, and GLUE benchmarks while maintaining a high parameter efficiency. |
Principled Understanding of Generalization for Generative Transformer Models in Arithmetic Reasoning Tasks (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models excel in arithmetic reasoning but their generalization capabilities are incompletely understood. |
| Approach: | They propose a theoretical framework for understanding the generalization behaviors of transformers in arithmetic tasks, focusing on length generalization. |
| Outcome: | The proposed framework can predict generalization behaviors in transformers with a high translation invariance and base mismatch in modular operations. |
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding (2025.findings-acl)
Copied to clipboard
| Challenge: | Vision Language Models struggle with visual arithmetic, seemingly simple tasks like object counting or length comparison, which are essential for relevant complex tasks like chart understanding and geometric reasoning. |
| Approach: | They propose a novel post-training strategy inspired by Piaget’s theory of cognitive development that trains VLMs to recognize invariant properties under visual transformations. |
| Outcome: | The proposed approach outperforms supervised fine-tuning methods while requiring 60% less training data. |
Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods do not differentiate question difficulty when designing prompting methods for them. |
| Approach: | They propose an adaptive method to improve large language models for reasoning problems by measuring question difficulty and tailoring demonstration set construction and difficulty-adapted retrieval strategies. |
| Outcome: | The proposed method shows an absolute improvement of up to 5.5% on arithmetic reasoning, 7.4% on symbolic reasoning, and 2.3% on commonsense reasoning. |
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency (2023.acl-long)
Copied to clipboard
| Challenge: | Numeracy is the most prevalent form of non-linguistic information embedded in textual corpora. |
| Approach: | They propose a framework for non-linguistic skill injection for LLMs that incorporates information-theoretic interventions and skill-specific losses to enable the learning of strict arithmetic reasoning. |
| Outcome: | The proposed model outperforms the state-of-the-art on injected non-linguistic skills and on linguistic knowledge retention with a fraction of the non-language training data (1/4) and zero additional synthetic linguistic training data. |
An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs (2024.acl-long)
Copied to clipboard
| Challenge: | Prior work has focused on ablating components in the CoT prompt, but the reason why these components are important to LLM reasoning is not explored. |
| Approach: | They investigate "neuron activation" as a lens to provide a unified explanation to previous work . they propose an approach to automatically identify neurons that imply arithmetic reasoning . |
| Outcome: | The proposed approach can explain the importance of components in a CoT prompt . it also automatically identifies neurons that imply arithmetic reasoning . |
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)
Copied to clipboard
Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization. |
| Approach: | They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question. |
| Outcome: | The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks. |
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown impressive abilities on various tasks. however, fundamentally improving them depends on high-quality datasets or computationally expensive fine-tuning. |
| Approach: | They propose a framework to let Large Language Models self-improve without annotated datasets or parameter updates. |
| Outcome: | The proposed framework can help ChatGPT significantly improve its abilities in arithmetic reasoning, commonsense reasoning, factual reasoning, and natural language inference. |
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)
Copied to clipboard
Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following. |
| Approach: | They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply. |
| Outcome: | The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
Copied to clipboard
Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
Bridging the Gap between Different Vocabularies for LLM Ensemble (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to combine large language models with complete outputs have limited effectiveness . lexical gaps between different LLMs hinder dynamic correction and enhancement . |
| Approach: | They propose a method to ensemble large language models via Vocabulary Alignment (EVA) they learn mappings between vocabularies of different LLMs with overlapping tokens . |
| Outcome: | The proposed method bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step. |
DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph methods lack adaptability in knowledge-intensive tasks with multiple entities and implicit multi-hop relations. |
| Approach: | They propose a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. |
| Outcome: | DS-MHP outperforms baselines and state-of-the-art methods on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning. |
SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are widely deployed as domain-specific agents, but evaluation of their capabilities in such contexts has not been fully explored. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to follow instructions and make decisions in real-world scenarios. |
| Outcome: | The proposed benchmark is constructed from real-world business data and adapted into 23 complex SOP scenarios. |
Self-training Language Models for Arithmetic Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work improves the reasoning capabilities of language models by scaling training data to more diverse or complex collections, but reaching further improvements becomes exceedingly expensive. |
| Approach: | They propose to use implicit feedback to improve models' reasoning capabilities by training from implicit feedback. |
| Outcome: | The proposed model can reach a correct result in +13.9% and +25.9% more cases than previous models, underlining the importance of actuality of self-training feedback. |
Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination (2024.emnlp-main)
Copied to clipboard
| Challenge: | a series of investigations into an interesting phenomenon where performance increases in large language models when providing a prompt that causes and exploits hallucination. |
| Approach: | They propose a null-shot prompting approach that intentionally instructs LLMs to look at and utilize information from a nil section. |
| Outcome: | The proposed approach causes and exploits hallucination in large language models on a range of tasks including arithmetic reasoning, commonsense reasoning, and reading comprehension. |
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. |
| Approach: | They propose to combine existing chain-of-thought datasets into a unified format that can be used to train and evaluate open-source calculator-using models. |
| Outcome: | The proposed model doubles the accuracy of generating correct results compared to baseline models. |
Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. |
| Approach: | They propose a framework that explores decisions’ consequences from multiple stakeholder perspectives and a SKIG framework to enhance moral reasoning in large language models. |
| Outcome: | The proposed framework exhibits marked improvements compared to baselines across different language models and benchmarks. |
Leveraging LLM Reasoning Enhances Personalized Recommender Systems (2024.findings-acl)
Copied to clipboard
Alicia Tsai, Adam Kraft, Long Jin, Chenwei Cai, Anahita Hosseini, Taibai Xu, Zemin Zhang, Lichan Hong, Ed H. Chi, Xinyang Yi
| Challenge: | Recent advances have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. |
| Approach: | They propose to use Large Language Models to perform tasks with subjectivity and personalized preferences as inputs to RecSys. |
| Outcome: | The proposed framework aligns with real human judgment on the coherence and faithfulness of LLM reasoning responses. |
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) prompting is a new approach to prompt large language models (LLMs) but most studies rely on human-annotated rational chains to prompt LLMs . |
| Approach: | They propose a method that augments rational chains from a small labeled dataset and pruning low-quality chains to construct a pool of machine generated rationale chains based on the labels. |
| Outcome: | The proposed method can bypass human engineering of CoT by automatically augmenting rational chains from a small labeled dataset, and pruning low-quality chains to construct a candidate pool of machine generated rationale chains based on the labels. |
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. |
Evaluating LLMs’ Mathematical and Coding Competency through Ontology-guided Interventions (2025.findings-acl)
Copied to clipboard
| Challenge: | Current large language models have shown impressive performance on logical reasoning benchmarks . however, the true depth of their competencies and robustness in reasoning tasks remains an open question . |
| Approach: | They propose a general ontology of perturbations and a semi-automatic method to apply perturbations to arithmetic reasoning and code generation datasets to test their LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing models on arithmetic reasoning and code generation tasks. |
QDMR-based Planning-and-Solving Prompting for Complex Reasoning Tasks (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing Plan-and-Solve prompting methods are difficult to implement for complex questions. |
| Approach: | They propose a plan-and-solve prompting method based on Question Decomposition Meaning Representation (QDMR) it allows LLM to generate a QDMR graph to represent problem-solving logic . |
| Outcome: | The proposed method can represent and execute the problem-solving logic of complex questions more accurately than existing methods. |
POLARIS: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair (2026.findings-acl)
Copied to clipboard
| Challenge: | Gödel agent Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark. |
| Approach: | They propose a Gödel agent that performs policy repair via experience abstraction . Polaris makes policy level changes with small, auditable patches that persist in the policy . |
| Outcome: | The proposed agent improves on MGSM, DROP, GPQA, and LitBench models over the base policy and competitive baselines. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
Copied to clipboard
Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |