Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. |
| Approach: | They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. |
| Outcome: | The proposed model outperforms standard GPT-4o-mini on the Loong benchmark and fine tunes LLaMA-3.1-8B-Instruct on the model, achieving a 28.0% gain on the financial subset. |
Similar Papers
Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision (2025.findings-emnlp)
Copied to clipboard
Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu, Haosheng Zou, Junfeng Ran, null XWang, Lin Sun, Xiangzheng Zhang, Sujian Li
| Challenge: | Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks. |
| Approach: | They propose a chain-of-thought framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance. |
| Outcome: | The proposed framework generalizes across most long-context scenarios and amplifys with increasing context length. |
Self-Taught Agentic Long Context Understanding (2025.acl-long)
Copied to clipboard
Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Long chain-of-thought (CoT) prompting often slows or even degrades performance on fast, intuitive "System-1" tasks. |
| Approach: | They introduce a method that deliberately restricts reasoning to a small, fixed set of connector phrases, steering the model toward concise and well-structured explanations. |
| Outcome: | The method achieves 85% on GSM8K and 40% on GPQA while also surpassing the baseline by over 20%. |
DRT: Deep Reasoning Translation via Long Chain-of-Thought (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. |
| Approach: | They propose to use Qwen2.5 and LLama-3.1 as backbones to train long thought models to bring the success of long chain-of-thought (CoT) to neural machine translation. |
| Outcome: | The proposed model outperforms vanilla LLMs and LLM models which are fine-tuning on paired sentences without long thought and outperformed vanilla LRMs. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
Copied to clipboard
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work finds that long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. |
| Approach: | They propose a representation engineering method to unleash the general long CoT reasoning capabilities of LLMs. |
| Outcome: | The proposed method is effective in in-domain and cross-domain scenarios. |
Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks. |
| Approach: | They propose a training framework that transfers reasoning capabilities from proxy contexts to full long contexts. |
| Outcome: | The proposed framework outperforms baseline models with reduced computational overhead. |
Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for extending the maximum context lengths of language models are lacking a strong baseline for in-context few-shot classification and on more challenging Chain-of-Thought reasoning, such as HotpotQA, deteriorate question miscomprehension and false inference. |
| Approach: | They propose to harness window-wise attention and positional embedding techniques to extend the maximum context lengths of language models. |
| Outcome: | The proposed method is able to extend the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques. |
LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)
Copied to clipboard
| Challenge: | Long-context processing ability has emerged as a significant challenge for large language models. |
| Approach: | They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them . |
| Outcome: | The proposed pipeline eliminates distractions and improves reasoning chains. |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
Copied to clipboard
| 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. |