Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to problem-solving for large language models fail to provide accurate reasoning and factual accuracy. |
| Approach: | They propose a framework that leverages fine-tuned critic models to guide reasoning and retrieval processes. |
| Outcome: | The proposed framework outperforms baselines on domain-knowledge-intensive tasks . it can be used to iterate retrieval and reasoning, and improve retrieval relevance . |
Similar Papers
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) often produce factual errors due to limited internal knowledge. |
| Approach: | They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation. |
| Outcome: | The proposed framework improves the accuracy of large language models with external knowledge sources. |
Eliciting Critical Reasoning in Retrieval-Augmented Generation via Contrastive Explanations (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent studies show that LLMs struggle to critically analyse RAG-based in-context information. |
| Approach: | They propose a framework that elicits critical arguments in RAG via contrastive explanations . they propose CRAG to retrieve relevant documents given a query and generate explanations that explicitly contrast relevance of passages to support the final answer. |
| Outcome: | The proposed framework improves state-of-the-art RAG models while requiring significantly fewer prompts and demonstrations and robust to perturbations in the retrieved documents. |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
Copied to clipboard
Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities. |
| Approach: | They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation. |
| Outcome: | The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement. |
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have raised concerns regarding the hallucination and flaws in their reasoning process. |
| Approach: | They propose a framework to learn planning-based reasoning through Direct Preference Optimization on collected trajectories, which are ranked according to synthesized process rewards. |
| Outcome: | The proposed model surpasses GPT-3.5-Turbo on logical reasoning benchmarks on a set of logically-based reasoning tasks. |
D2Plan: Dual-Agent Dynamic Global Planning for Complex Retrieval-Augmented Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in reinforcement learning (RL) have empowered Large Language Models (LLMs) with the capability to perform autonomous retrieval during reasoning tasks. |
| Approach: | They propose a "D2Plan" paradigm for retrieval-augmented reasoning that integrates a 'Reasoner' and a'Purifier' |
| Outcome: | Experiments show that the proposed paradigm improves on QA benchmarks. |
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation (2025.naacl-long)
Copied to clipboard
Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities across various cognitive tasks. |
| Approach: | They propose a high-quality evaluation dataset to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. |
| Outcome: | The proposed framework improves performance in end-to-end RAG scenarios. |
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated excellent performance in numerous tasks but the parameterized knowledge stored within LLMs may be incomplete and hard to incorporate up-to-date knowledge. |
| Approach: | They propose a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model’s problem-solving capabilities. |
| Outcome: | The proposed method outperforms existing benchmarks on GPT3.5, Llama2 and other large language models significantly enhancing factual reasoning capabilities and reducing hallucinations. |
PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for decision making require complex data analysis. |
| Approach: | They propose a method that generates the plan for decision making as the first step and retrieves the queries for data analysis as the second step. |
| Outcome: | The proposed method outperforms the state-of-the-art iterative plan-then-retrieval augmented generation method by 15.8% and 7.4% respectively. |
RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable performance across a wide range of downstream tasks. |
| Approach: | They propose a framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously. |
| Outcome: | The proposed framework improves RAG capabilities autonomously by leveraging a critic-guided agentic workflow. |
Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)
Copied to clipboard
Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li
| Challenge: | Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL). |
| Approach: | They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer. |
| Outcome: | Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks . |