| Challenge: | Empirical results on mathematical reasoning benchmarks substantiate the efficacy of Large language models (LLMs). |
| Approach: | They propose a framework that combines an LLM with a process-based verifier to generate plausible candidates and provide timely process-driven feedback to distinguish desirable and undesirable outputs. |
| Outcome: | Empirical results show that LLM2 improves accuracy on GSM8K and self-consistency increases major@20 accuracy. |
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LM2: A Simple Society of Language Models Solves Complex Reasoning (2024.emnlp-main)
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| Challenge: | Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning. |
| Approach: | They propose a language-based decomposition, solution and verification framework that modularizes the decomposer, solution, and verification into three different language models. |
| Outcome: | The proposed model outperforms existing methods on in- and out-domain reasoning problems, outperforming the best baselines by 8.1% on MATH, 7.71% on JEEBench, and 9.7% on MedQA problems. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
Drift: Enhancing LLM Faithfulness in Rationale Generation via Dual-Reward Probabilistic Inference (2025.acl-long)
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| Challenge: | Existing approaches to improving LLM faithfulness rely on superficial calibration methods or costly retraining. |
| Approach: | They propose a probabilistic inference paradigm that leverages task-specific and lookahead rewards to ensure that LLM-generated rationales are more faithful to model decisions. |
| Outcome: | The proposed model improves both accuracy and faithfulness of Large Language Models (LLMs) on three reasoning tasks. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations (2025.acl-long)
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| Challenge: | Recent work has shown that the interaction of large language models (LLMs) with theorem provers (TPs) can help verify and improve the validity of NLI explanations. |
| Approach: | They propose to use logical expressions to guide LLMs in generating structured proof sketches and to use them to improve their accuracy. |
| Outcome: | The proposed strategies improve autoformalisation, syntactic errors and explanation refinement over the state-of-the-art model. |
Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings. |
| Approach: | They propose a training recipe for an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM. |
| Outcome: | The proposed model significantly speeds up inference time and out-of-domain speedup across various languages. |
LLM Evaluate: An Industry-Focused Evaluation Tool for Large Language Models (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks in recent years. |
| Approach: | They propose to build an on-premise system for LLM evaluation to address the challenges in the evaluation of LLMs in real-world industrial settings. |
| Outcome: | The proposed evaluation system protects customer privacy and protects data integrity in real-world industrial environments. |
Improving Consistency in LLM Inference using Probabilistic Tokenization (2025.findings-naacl)
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| Challenge: | Prior work has shown that probabilistic tokenizations can generate multiple tokenization of the same input string. |
| Approach: | They propose a method to leverage the multiple tokenization capabilities of modern LLM tokenizers. |
| Outcome: | The proposed method improves the self-consistency of large language models by generating multiple tokenizations. |
Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)
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| Challenge: | a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
| Approach: | They propose a lightweight technique that trains only new token embeddings on axioms . they train only new embeddables and evaluate them on unseen tasks . |
| Outcome: | The proposed technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |